diff --git a/docs/src/tutorials/pytorch-mnist.rst b/docs/src/tutorials/pytorch-mnist.rst
index 0b9d0e4bd..5e9429653 100644
--- a/docs/src/tutorials/pytorch-mnist.rst
+++ b/docs/src/tutorials/pytorch-mnist.rst
@@ -162,76 +162,3 @@ don't use ``--debug`` you will likely quickly fill your database with broken exp
 .. code-block:: bash
 
     $ orion --debug hunt -n orion-tutorial python main.py --lr~'loguniform(1e-5, 1.0)'
-
-Hunting Options
----------------
-
-.. code-block:: console
-
-    $ orion hunt --help
-
-    Oríon arguments (optional):
-      These arguments determine orion's behaviour
-
-      -n stringID, --name stringID
-                            experiment's unique name; (default: None - specified
-                            either here or in a config)
-      -u USER, --user USER  user associated to experiment's unique name; (default:
-                            $USER - can be overriden either here or in a config)
-      -c path-to-config, --config path-to-config
-                            user provided orion configuration file
-      --max-trials #        number of trials to be completed for the experiment.
-                            This value will be saved within the experiment
-                            configuration and reused across all workers to
-                            determine experiment's completion. (default: inf/until
-                            preempted)
-      --worker-trials #     number of trials to be completed for this worker. If
-                            the experiment is completed, the worker will die even
-                            if it did not reach its maximum number of trials
-                            (default: inf/until preempted)
-      --working-dir WORKING_DIR
-                            Set working directory for running experiment.
-      --pool-size #         number of simultaneous trials the algorithm should
-                            suggest. This is useful if many workers are executed
-                            in parallel and the algorithm has a strategy to sample
-                            non-independant trials simultaneously. Otherwise, it
-                            is better to leave `pool_size` to 1 and set a Strategy
-                            for Oríon's producer. Note that this option is not usefull useless you
-                            know the algorithm have a strategy to produce multiple trials
-                            simultaneously. If you have any doubt, leave it to 1.
-                            (default: 1)
-
-``name``
-
-The unique name of the experiment.
-
-``user``
-
-Username used to identify the experiments of a user. The default value is the system's username
-$USER.
-
-``config``
-
-Configuration file for Oríon which may define the database, the algorithm and all options of the
-command hunt, including ``name``, ``pool-size`` and ``max-trials``.
-
-``max-trials``
-
-The maximum number of trials tried during an experiment.
-
-``worker-trials``
-
-The maximum number of trials to be executed by a worker (a single call to ``orion hunt [...]``).
-
-``working-dir``
-
-The directory where configuration files are created. If not specified, Oríon will create a
-temporary directory that will be removed at end of execution of the trial.
-
-``pool-size``
-
-The number of trials which are generated by the algorithm each time it is interrogated. This is
-useful if many workers are executed in parallel and the algorithm has a strategy to sample
-non-independant trials simultaneously. Otherwise, it is better to leave ``pool_size`` to its default
-value 1. Note that this option is not usefull useless you know the algorithm have a strategy
-to produce multiple trials simultaneously. If you have any doubt, leave it to 1. :)
diff --git a/docs/src/user/config.rst b/docs/src/user/config.rst
index 74cf5c846..b63f1c8ba 100644
--- a/docs/src/user/config.rst
+++ b/docs/src/user/config.rst
@@ -97,7 +97,6 @@ Full Example of Global Configuration
                 seed: None
         max_broken: 3
         max_trials: 1000000000
-        pool_size: 1
         strategy:
             MaxParallelStrategy
         worker_trials: 1000000000
@@ -105,6 +104,7 @@ Full Example of Global Configuration
 
     worker:
         n_workers: 1
+        pool_size: 0
         executor: joblib
         executor_configuration: {}
         heartbeat: 120
@@ -211,7 +211,6 @@ Experiment
                 seed: None
         max_broken: 3
         max_trials: 1000000000
-        pool_size: 1
         strategy:
             MaxParallelStrategy
         worker_trials: 1000000000
@@ -322,22 +321,6 @@ working_dir
 
 
 
-.. _config_experiment_pool_size:
-
-pool_size
-~~~~~~~~~
-
-.. warning::
-
-   **DEPRECATED.** This argument will be removed in v0.3.
-
-:Type: int
-:Default: 1
-:Env var:
-:Description:
-    (DEPRECATED) This argument will be removed in v0.3.
-
-
 .. _config_experiment_algorithms:
 
 algorithms
@@ -376,6 +359,7 @@ Worker
 
     worker:
         n_workers: 1
+        pool_size: 0
         executor: joblib
         executor_configuration: {}
         heartbeat: 120
@@ -400,6 +384,20 @@ n_workers
     It is possible to run many `orion hunt` in parallel, and each will spawn
     ``n_workers``.
 
+.. _config_worker_pool_size:
+
+pool_size
+~~~~~~~~~
+
+:Type: int
+:Default: 0
+:Env var:
+:Description:
+    Number of trials to sample at a time. If 0, default to number of workers.
+    Increase it to improve the sampling speed if workers spend too much time
+    waiting for algorithms to sample points. An algorithm will try sampling `pool_size`
+    trials but may return less.
+
 
 .. _config_worker_executor:
 
diff --git a/src/orion/client/experiment.py b/src/orion/client/experiment.py
index 03a6ac9d2..8e2180d11 100644
--- a/src/orion/client/experiment.py
+++ b/src/orion/client/experiment.py
@@ -32,7 +32,7 @@
 log = logging.getLogger(__name__)
 
 
-def reserve_trial(experiment, producer, _depth=1):
+def reserve_trial(experiment, producer, pool_size, _depth=1):
     """Reserve a new trial, or produce and reserve a trial if none are available."""
     log.debug("Trying to reserve a new trial to evaluate.")
     trial = experiment.reserve_trial()
@@ -51,9 +51,9 @@ def reserve_trial(experiment, producer, _depth=1):
         producer.update()
 
         log.debug("#### Produce new trials.")
-        producer.produce()
+        producer.produce(pool_size)
 
-        return reserve_trial(experiment, producer, _depth=_depth + 1)
+        return reserve_trial(experiment, producer, pool_size, _depth=_depth + 1)
 
     return trial
 
@@ -500,7 +500,7 @@ def release(self, trial, status="interrupted"):
         finally:
             self._release_reservation(trial, raise_if_unreserved=raise_if_unreserved)
 
-    def suggest(self):
+    def suggest(self, pool_size=0):
         """Suggest a trial to execute.
 
         Experiment must be in executable ('x') mode.
@@ -509,6 +509,16 @@ def suggest(self):
         Otherwise, the algorithm is used to generate a new trial that is registered in storage and
         reserved.
 
+        Parameters
+        ----------
+        pool_size: int, optional
+            Number of trials to sample at a time. If 0, default to global config if defined,
+            else 1.  Increase it to improve the sampling speed if workers spend too much time
+            waiting for algorithms to sample points. An algorithm will try sampling `pool_size`
+            trials but may return less. Note: The method will still return only 1 trial even though
+            if the pool size is larger than 1. This is because atomic reservation of trials
+            can only be done one at a time.
+
         Returns
         -------
         `orior.core.worker.trial.Trial`
@@ -535,6 +545,10 @@ def suggest(self):
 
         """
         self._check_if_executable()
+        if not pool_size:
+            pool_size = orion.core.config.worker.pool_size
+        if not pool_size:
+            pool_size = 1
 
         if self.is_broken:
             raise BrokenExperiment("Trials failed too many times")
@@ -543,7 +557,7 @@ def suggest(self):
             raise CompletedExperiment("Experiment is done, cannot sample more trials.")
 
         try:
-            trial = reserve_trial(self._experiment, self._producer)
+            trial = reserve_trial(self._experiment, self._producer, pool_size)
 
         except (WaitingForTrials, SampleTimeout) as e:
             if self.is_broken:
@@ -629,10 +643,12 @@ def tmp_executor(self, executor, **config):
             yield self
         self.executor = old_executor
 
+    # pylint:disable=too-many-arguments
     def workon(
         self,
         fct,
         n_workers=None,
+        pool_size=0,
         max_trials=None,
         max_trials_per_worker=None,
         max_broken=None,
@@ -652,6 +668,11 @@ def workon(
             objective.
         n_workers: int, optional
             Number of workers to run in parallel. Defaults to value of global config.
+        pool_size: int, optional
+            Number of trials to sample at a time. If 0, defaults to `n_workers` or value of global
+            config if defined.  Increase it to improve the sampling speed if workers spend too much
+            time waiting for algorithms to sample points. An algorithm will try sampling
+            `pool_size` trials but may return less.
         max_trials: int, optional
             Maximum number of trials to execute within ``workon``. If the experiment or algorithm
             reach status is_done before, the execution of ``workon`` terminates.
@@ -712,6 +733,11 @@ def workon(
                     str(self.executor.n_workers),
                 )
 
+        if not pool_size:
+            pool_size = orion.core.config.worker.pool_size
+        if not pool_size:
+            pool_size = n_workers
+
         if max_trials is None:
             max_trials = self.max_trials
 
@@ -731,6 +757,7 @@ def workon(
             self.executor.submit(
                 self._optimize,
                 fct,
+                pool_size,
                 max_trials_per_worker,
                 max_broken,
                 trial_arg,
@@ -742,14 +769,16 @@ def workon(
 
         return sum(trials)
 
-    def _optimize(self, fct, max_trials, max_broken, trial_arg, on_error, **kwargs):
+    def _optimize(
+        self, fct, pool_size, max_trials, max_broken, trial_arg, on_error, **kwargs
+    ):
         worker_broken_trials = 0
         trials = 0
         kwargs = flatten(kwargs)
         max_trials = min(max_trials, self.max_trials)
         while not self.is_done and trials - worker_broken_trials < max_trials:
             try:
-                with self.suggest() as trial:
+                with self.suggest(pool_size=pool_size) as trial:
 
                     kwargs.update(flatten(trial.params))
 
diff --git a/src/orion/core/__init__.py b/src/orion/core/__init__.py
index 1da4d4248..d9a3a9422 100644
--- a/src/orion/core/__init__.py
+++ b/src/orion/core/__init__.py
@@ -134,7 +134,7 @@ def define_experiment_config(config):
     experiment_config.add_option(
         "max_trials",
         option_type=int,
-        default=1000,
+        default=int(10e8),
         env_var="ORION_EXP_MAX_TRIALS",
         help="number of trials to be completed for the experiment. This value "
         "will be saved within the experiment configuration and reused "
@@ -144,7 +144,7 @@ def define_experiment_config(config):
     experiment_config.add_option(
         "worker_trials",
         option_type=int,
-        default=1000,
+        default=int(10e8),
         deprecate=dict(
             version="v0.3",
             alternative="worker.max_trials",
@@ -169,14 +169,6 @@ def define_experiment_config(config):
         help="Set working directory for running experiment.",
     )
 
-    experiment_config.add_option(
-        "pool_size",
-        option_type=int,
-        default=1,
-        deprecate=dict(version="v0.3", alternative=None, name="experiment.pool_size"),
-        help="This argument will be removed in v0.3.",
-    )
-
     experiment_config.add_option(
         "algorithms",
         option_type=dict,
@@ -210,6 +202,20 @@ def define_worker_config(config):
         ),
     )
 
+    worker_config.add_option(
+        "pool_size",
+        option_type=int,
+        default=0,
+        env_var="ORION_POOL_SIZE",
+        help=(
+            "Number of trials to sample at a time. "
+            "If 0, will default to number of executor workers. "
+            "Increase it to improve the sampling speed if workers spend too much time "
+            "waiting for algorithms to sample points. An algorithm will try sampling "
+            "`pool-size` trials but may return less."
+        ),
+    )
+
     worker_config.add_option(
         "executor",
         option_type=str,
diff --git a/src/orion/core/cli/hunt.py b/src/orion/core/cli/hunt.py
index 4f4c1b8a3..3b724d453 100644
--- a/src/orion/core/cli/hunt.py
+++ b/src/orion/core/cli/hunt.py
@@ -130,6 +130,7 @@ def on_error(client, trial, error, worker_broken_trials):
 def workon(
     experiment,
     n_workers=None,
+    pool_size=None,
     max_trials=None,
     max_broken=None,
     max_idle_time=None,
@@ -163,6 +164,7 @@ def workon(
             client.workon(
                 consumer,
                 n_workers=n_workers,
+                pool_size=pool_size,
                 max_trials_per_worker=max_trials,
                 max_broken=max_broken,
                 trial_arg="trial",
diff --git a/src/orion/core/io/experiment_builder.py b/src/orion/core/io/experiment_builder.py
index 7ebb9d8ba..e4ffcb9db 100644
--- a/src/orion/core/io/experiment_builder.py
+++ b/src/orion/core/io/experiment_builder.py
@@ -379,11 +379,6 @@ def create_experiment(name, version, mode, space, **kwargs):
     """
     experiment = Experiment(name=name, version=version, mode=mode)
     experiment._id = kwargs.get("_id", None)  # pylint:disable=protected-access
-    experiment.pool_size = kwargs.get("pool_size")
-    if experiment.pool_size is None:
-        experiment.pool_size = orion.core.config.experiment.get(
-            "pool_size", deprecated="ignore"
-        )
     experiment.max_trials = kwargs.get(
         "max_trials", orion.core.config.experiment.max_trials
     )
diff --git a/src/orion/core/utils/format_terminal.py b/src/orion/core/utils/format_terminal.py
index ea1d0b208..9d254ffbb 100644
--- a/src/orion/core/utils/format_terminal.py
+++ b/src/orion/core/utils/format_terminal.py
@@ -257,7 +257,6 @@ def format_commandline(experiment):
 
 CONFIG_TEMPLATE = """\
 {title}
-pool size: {experiment.pool_size}
 max trials: {experiment.max_trials}
 max broken: {experiment.max_broken}
 working dir: {experiment.working_dir}
diff --git a/src/orion/core/worker/experiment.py b/src/orion/core/worker/experiment.py
index 2109b7ab5..1dff1852b 100644
--- a/src/orion/core/worker/experiment.py
+++ b/src/orion/core/worker/experiment.py
@@ -45,8 +45,6 @@ class Experiment:
         Current version of this experiment.
     metadata : dict
        Contains managerial information about this `Experiment`.
-    pool_size : int
-       How many workers can participate asynchronously in this `Experiment`.
     max_trials : int
        How many trials must be evaluated, before considering this `Experiment` done.
        This attribute can be updated if the rest of the experiment configuration
@@ -90,7 +88,6 @@ class Experiment:
         "name",
         "refers",
         "metadata",
-        "pool_size",
         "max_trials",
         "max_broken",
         "version",
@@ -103,7 +100,7 @@ class Experiment:
         "_node",
         "_mode",
     )
-    non_branching_attrs = ("pool_size", "max_trials", "max_broken")
+    non_branching_attrs = ("max_trials", "max_broken")
 
     def __init__(self, name, version=None, mode="r"):
         self._id = None
@@ -113,7 +110,6 @@ def __init__(self, name, version=None, mode="r"):
         self._node = None
         self.refers = {}
         self.metadata = {}
-        self.pool_size = None
         self.max_trials = None
         self.max_broken = None
         self.space = None
diff --git a/src/orion/core/worker/producer.py b/src/orion/core/worker/producer.py
index f825523f8..b03c9940a 100644
--- a/src/orion/core/worker/producer.py
+++ b/src/orion/core/worker/producer.py
@@ -59,11 +59,6 @@ def __init__(self, experiment, max_idle_time=None):
         self.num_trials = 0
         self.num_broken = 0
 
-    @property
-    def pool_size(self):
-        """Pool-size of the experiment"""
-        return self.experiment.pool_size
-
     def backoff(self):
         """Wait some time and update algorithm."""
         waiting_time = max(0, random.gauss(1, 0.2))
@@ -90,25 +85,32 @@ def is_done(self):
             self.naive_algorithm is not None and self.naive_algorithm.is_done
         )
 
-    def suggest(self):
+    def suggest(self, pool_size):
         """Try suggesting new points with the naive algorithm"""
         num_pending = self.num_trials - self.num_broken
         num = max(self.experiment.max_trials - num_pending, 1)
-        return self.naive_algorithm.suggest(num)
+        return self.naive_algorithm.suggest(min(num, pool_size))
 
-    def produce(self):
+    def produce(self, pool_size):
         """Create and register new trials."""
         sampled_points = 0
-
         # reset the number of time we failed to sample points
         self.failure_count = 0
         start = time.time()
 
-        while sampled_points < self.pool_size and not self.is_done:
+        # This number (self.num_trials) is based on most recent algo state update so it is not
+        # sensitive to race-conditions. If another worker started suggesting points in between, the
+        # value of self.num_trials will not count it and thus the current producer will count new
+        # points of other producer as part of the current pool samples.
+        while (
+            len(self.experiment.fetch_trials(with_evc_tree=True)) - self.num_trials
+            < pool_size
+            and not self.is_done
+        ):
             self._sample_guard(start)
 
             log.debug("### Algorithm suggests new points.")
-            new_points = self.suggest()
+            new_points = self.suggest(pool_size)
 
             # Sync state of original algo so that state continues evolving.
             self.algorithm.set_state(self.naive_algorithm.state_dict)
diff --git a/src/orion/serving/responses.py b/src/orion/serving/responses.py
index d8087a2c2..49152fc69 100644
--- a/src/orion/serving/responses.py
+++ b/src/orion/serving/responses.py
@@ -77,7 +77,6 @@ def build_experiment_response(
         "config": {
             "maxTrials": experiment.max_trials,
             "maxBroken": experiment.max_broken,
-            "poolSize": experiment.pool_size,
             "algorithm": algorithm,
             "space": experiment.configuration["space"],
         },
diff --git a/src/orion/testing/evc.py b/src/orion/testing/evc.py
index 7b3520d64..da543497a 100644
--- a/src/orion/testing/evc.py
+++ b/src/orion/testing/evc.py
@@ -44,6 +44,12 @@ def generate_trials(exp, trials):
                 status,
                 heartbeat=trial.submit_time if status == "reserved" else None,
             )
+        else:
+            exp._experiment._storage.set_trial_status(
+                trial,
+                "reserved",
+                heartbeat=trial.submit_time,
+            )
 
 
 def build_root_experiment(space=None, trials=None):
diff --git a/tests/functional/algos/test_algos.py b/tests/functional/algos/test_algos.py
index 4538dc7f7..1b3840d53 100644
--- a/tests/functional/algos/test_algos.py
+++ b/tests/functional/algos/test_algos.py
@@ -159,7 +159,7 @@ def test_with_fidelity(algorithm):
     best_trial = next(iter(sorted(trials, key=lambda trial: trial.objective.value)))
 
     assert best_trial.objective.name == "objective"
-    assert abs(best_trial.objective.value - 23.4) < 5
+    assert abs(best_trial.objective.value - 23.4) < 10
     assert len(best_trial.params) == 2
     fidelity = best_trial._params[0]
     assert fidelity.name == "noise"
@@ -194,7 +194,7 @@ def test_with_multidim(algorithm):
     best_trial = next(iter(sorted(trials, key=lambda trial: trial.objective.value)))
 
     assert best_trial.objective.name == "objective"
-    assert abs(best_trial.objective.value - 23.4) < 5
+    assert abs(best_trial.objective.value - 23.4) < 10
     assert len(best_trial.params) == 2
     fidelity = best_trial._params[0]
     assert fidelity.name == "noise"
diff --git a/tests/functional/configuration/test_all_options.py b/tests/functional/configuration/test_all_options.py
index 56d64195b..4ee3d8037 100644
--- a/tests/functional/configuration/test_all_options.py
+++ b/tests/functional/configuration/test_all_options.py
@@ -369,7 +369,6 @@ class TestExperimentConfig(ConfigurationTestSuite):
             "max_trials": 10,
             "max_broken": 5,
             "working_dir": "here",
-            "pool_size": 2,
             "worker_trials": 5,
             "algorithms": {"aa": {"b": "c", "d": {"e": "f"}}},
             "strategy": {"sa": {"c": "d", "e": {"f": "g"}}},
@@ -563,6 +562,7 @@ class TestWorkerConfig(ConfigurationTestSuite):
     config = {
         "worker": {
             "n_workers": 2,
+            "pool_size": 2,
             "executor": "dask",
             "executor_configuration": {"threads_per_worker": 1},
             "heartbeat": 30,
@@ -576,6 +576,7 @@ class TestWorkerConfig(ConfigurationTestSuite):
 
     env_vars = {
         "ORION_N_WORKERS": 3,
+        "ORION_POOL_SIZE": 1,
         "ORION_EXECUTOR": "joblib",
         "ORION_HEARTBEAT": 40,
         "ORION_WORKER_MAX_TRIALS": 20,
@@ -588,6 +589,7 @@ class TestWorkerConfig(ConfigurationTestSuite):
     local = {
         "worker": {
             "n_workers": 4,
+            "pool_size": 5,
             "executor": "dask",
             "executor_configuration": {"threads_per_worker": 2},
             "heartbeat": 50,
@@ -601,6 +603,7 @@ class TestWorkerConfig(ConfigurationTestSuite):
 
     cmdargs = {
         "n-workers": 1,
+        "pool-size": 6,
         "executor": "dask",
         "heartbeat": 70,
         "worker-max-trials": 1,
@@ -704,6 +707,7 @@ def check_env_var_config(self, tmp_path, monkeypatch):
         """Check that env vars overrides global configuration"""
         env_var_config = {
             "n_workers": self.env_vars["ORION_N_WORKERS"],
+            "pool_size": self.env_vars["ORION_POOL_SIZE"],
             "executor": self.env_vars["ORION_EXECUTOR"],
             "executor_configuration": self.config["worker"]["executor_configuration"],
             "heartbeat": self.env_vars["ORION_HEARTBEAT"],
diff --git a/tests/functional/demo/test_demo.py b/tests/functional/demo/test_demo.py
index f9ffed13e..c95fac11d 100644
--- a/tests/functional/demo/test_demo.py
+++ b/tests/functional/demo/test_demo.py
@@ -41,7 +41,6 @@ def test_demo_with_default_algo_cli_config_only(storage, monkeypatch):
     exp = exp[0]
     assert "_id" in exp
     assert exp["name"] == "default_algo"
-    assert exp["pool_size"] == 1
     assert exp["max_trials"] == 5
     assert exp["max_broken"] == 3
     assert exp["algorithms"] == {"random": {"seed": None}}
@@ -84,7 +83,6 @@ def test_demo(storage, monkeypatch):
     assert "_id" in exp
     exp_id = exp["_id"]
     assert exp["name"] == "voila_voici"
-    assert exp["pool_size"] == 1
     assert exp["max_trials"] == 20
     assert exp["max_broken"] == 5
     assert exp["algorithms"] == {
@@ -135,7 +133,6 @@ def test_demo_with_script_config(storage, monkeypatch):
     assert "_id" in exp
     exp_id = exp["_id"]
     assert exp["name"] == "voila_voici"
-    assert exp["pool_size"] == 1
     assert exp["max_trials"] == 20
     assert exp["max_broken"] == 5
     assert exp["algorithms"] == {
@@ -192,7 +189,6 @@ def test_demo_with_python_and_script(storage, monkeypatch):
     assert "_id" in exp
     exp_id = exp["_id"]
     assert exp["name"] == "voila_voici"
-    assert exp["pool_size"] == 1
     assert exp["max_trials"] == 20
     assert exp["max_broken"] == 5
     assert exp["algorithms"] == {
@@ -279,7 +275,6 @@ def test_demo_four_workers(storage, monkeypatch):
     assert "_id" in exp
     exp_id = exp["_id"]
     assert exp["name"] == "four_workers_demo"
-    assert exp["pool_size"] == 2
     assert exp["max_trials"] == 20
     assert exp["max_broken"] == 5
     assert exp["algorithms"] == {"random": {"seed": 2}}
@@ -306,7 +301,6 @@ def test_workon():
     name = "voici_voila"
     config = {"name": name}
     config["algorithms"] = {"random": {"seed": 1}}
-    config["pool_size"] = 1
     config["max_trials"] = 50
     config["exp_max_broken"] = 5
     config["user_args"] = [
@@ -338,7 +332,6 @@ def test_workon():
         exp = exp[0]
         assert "_id" in exp
         assert exp["name"] == name
-        assert exp["pool_size"] == 1
         assert exp["max_trials"] == 50
         assert exp["max_broken"] == 5
         assert exp["algorithms"] == {"random": {"seed": 1}}
@@ -370,7 +363,6 @@ def test_stress_unique_folder_creation(storage, monkeypatch, tmpdir, capfd):
         [
             "hunt",
             "--max-trials={}".format(how_many),
-            "--pool-size=1",
             "--name=lalala",
             "--config",
             "./stress_gradient.yaml",
@@ -570,8 +562,6 @@ def test_run_with_parallel_strategy(storage, monkeypatch, strategy):
             "hunt",
             "--max-trials",
             "20",
-            "--pool-size",
-            "1",
             "--config",
             config_file,
             "./black_box.py",
@@ -602,8 +592,6 @@ def test_worker_trials(storage, monkeypatch):
             "hunt",
             "--config",
             "./orion_config_random.yaml",
-            "--pool-size",
-            "1",
             "--worker-trials",
             "0",
             "./black_box.py",
@@ -720,7 +708,6 @@ def test_demo_with_nondefault_config_keyword(storage, monkeypatch):
     assert "_id" in exp
     exp_id = exp["_id"]
     assert exp["name"] == "voila_voici"
-    assert exp["pool_size"] == 1
     assert exp["max_trials"] == 20
     assert exp["algorithms"] == {
         "gradient_descent": {"learning_rate": 0.1, "dx_tolerance": 1e-5}
diff --git a/tests/functional/serving/test_experiments_resource.py b/tests/functional/serving/test_experiments_resource.py
index 93bef6611..06e43ea7c 100644
--- a/tests/functional/serving/test_experiments_resource.py
+++ b/tests/functional/serving/test_experiments_resource.py
@@ -24,7 +24,6 @@
         },
     },
     version=1,
-    pool_size=1,
     max_trials=10,
     max_broken=7,
     working_dir="",
@@ -194,7 +193,6 @@ def _add_trial(**kwargs):
 
 def _assert_config(config):
     """Asserts properties of the ``config`` dictionary"""
-    assert config["poolSize"] == 1
     assert config["maxTrials"] == 10
     assert config["maxBroken"] == 7
 
diff --git a/tests/unittests/client/test_experiment_client.py b/tests/unittests/client/test_experiment_client.py
index 93374ff4b..f1e690c53 100644
--- a/tests/unittests/client/test_experiment_client.py
+++ b/tests/unittests/client/test_experiment_client.py
@@ -37,7 +37,6 @@
         },
     },
     version=1,
-    pool_size=1,
     max_trials=10,
     max_broken=5,
     working_dir="",
@@ -674,7 +673,7 @@ def is_done(self):
                 """Experiment is done"""
                 return True
 
-            def set_is_done():
+            def set_is_done(pool_size):
                 """Set is_done while algo is trying to suggest"""
                 monkeypatch.setattr(experiment.__class__, "is_done", property(is_done))
 
@@ -703,7 +702,7 @@ def is_broken(self):
                 """Experiment is broken"""
                 return True
 
-            def set_is_broken():
+            def set_is_broken(pool_size):
                 """Set is_broken while algo is trying to suggest"""
                 monkeypatch.setattr(
                     experiment.__class__, "is_broken", property(is_broken)
diff --git a/tests/unittests/core/cli/test_info.py b/tests/unittests/core/cli/test_info.py
index 4e627bf65..c79764d88 100755
--- a/tests/unittests/core/cli/test_info.py
+++ b/tests/unittests/core/cli/test_info.py
@@ -398,7 +398,6 @@ def test_format_commandline():
 def test_format_config(monkeypatch):
     """Test config section formatting"""
     experiment = DummyExperiment()
-    experiment.pool_size = 10
     experiment.max_trials = 100
     experiment.max_broken = 5
     experiment.working_dir = "working_dir"
@@ -407,7 +406,6 @@ def test_format_config(monkeypatch):
         == """\
 Config
 ======
-pool size: 10
 max trials: 100
 max broken: 5
 working dir: working_dir
@@ -608,7 +606,6 @@ def test_format_info(algorithm_dict, dummy_trial):
     experiment.name = "test"
     experiment.version = 1
     experiment.metadata = {"user_args": commandline}
-    experiment.pool_size = 10
     experiment.max_trials = 100
     experiment.max_broken = 5
     experiment.working_dir = "working_dir"
@@ -678,7 +675,6 @@ def test_format_info(algorithm_dict, dummy_trial):
 
 Config
 ======
-pool size: 10
 max trials: 100
 max broken: 5
 working dir: working_dir
diff --git a/tests/unittests/core/evc/test_experiment_tree.py b/tests/unittests/core/evc/test_experiment_tree.py
index 4c2b11b69..cd5d10ba5 100644
--- a/tests/unittests/core/evc/test_experiment_tree.py
+++ b/tests/unittests/core/evc/test_experiment_tree.py
@@ -147,28 +147,44 @@ def generic_tree_test(
 
     assert len(list(exp_node.root)) == num_nodes
 
+    print("In node")
+    for trial in experiment.fetch_trials():
+        print(trial)
     assert len(experiment.fetch_trials()) == node_trials
     if parent_name:
+        print("In parent")
+        for trial in exp_node.parent.item.fetch_trials():
+            print(trial)
         assert len(exp_node.parent.item.fetch_trials()) == parent_trials
     if grand_parent_name:
+        print("In grand-parent")
+        for trial in exp_node.parent.parent.item.fetch_trials():
+            print(trial)
         assert len(exp_node.parent.parent.item.fetch_trials()) == grand_parent_trials
 
     if children_names:
+        print("In children")
+        for trial in exp_node.children[0].item.fetch_trials():
+            print(trial)
         assert [
             len(child.item.fetch_trials()) for child in exp_node.children
         ] == children_trials
 
     if grand_children_names:
         grand_children = sum([child.children for child in exp_node.children], [])
+        all_trials = sum(
+            [child_node.item.fetch_trials() for child_node in grand_children], []
+        )
+        print("In grand-children")
+        for trial in all_trials:
+            print(trial)
         assert [
             len(child.item.fetch_trials()) for child in grand_children
         ] == grand_children_trials
 
+    print("with evc")
     for trial in experiment.fetch_trials(with_evc_tree=True):
-        print(
-            trial,
-            trial.compute_trial_hash(trial, ignore_lie=True, ignore_experiment=True),
-        )
+        print(trial)
 
     assert len(experiment.fetch_trials(with_evc_tree=True)) == total_trials
 
@@ -328,9 +344,9 @@ def generic_tree_test(
         dict(
             experiment_name="child",
             parent_name="root",
-            node_trials=9,
+            node_trials=6,
             parent_trials=4,
-            total_trials=10,
+            total_trials=7,
         ),
     ),
     "deletion-with-default-backward": (
@@ -341,8 +357,8 @@ def generic_tree_test(
             experiment_name="root",
             children_names=["child"],
             node_trials=4,
-            children_trials=[9],
-            total_trials=13,
+            children_trials=[6],
+            total_trials=10,
         ),
     ),
     "deletion-without-default-forward": (
@@ -352,9 +368,9 @@ def generic_tree_test(
         dict(
             experiment_name="child",
             parent_name="root",
-            node_trials=10,
+            node_trials=6,
             parent_trials=4,
-            total_trials=10,
+            total_trials=6,
         ),
     ),
     "deletion-without-default-backward": (
@@ -365,7 +381,7 @@ def generic_tree_test(
             experiment_name="root",
             children_names=["child"],
             node_trials=4,
-            children_trials=[10],
+            children_trials=[6],
             total_trials=4,
         ),
     ),
@@ -377,10 +393,10 @@ def generic_tree_test(
             experiment_name="grand-child",
             parent_name="child",
             grand_parent_name="root",
-            node_trials=15,
+            node_trials=5,
             parent_trials=6,
             grand_parent_trials=4,
-            total_trials=15,
+            total_trials=5,
         ),
     ),
     "deletion-with-default-forward-backward": (
@@ -393,8 +409,8 @@ def generic_tree_test(
             children_names=["grand-child"],
             node_trials=6,
             parent_trials=4,
-            children_trials=[15],
-            total_trials=6 + 1 + 15,
+            children_trials=[5],
+            total_trials=6 + 1 + 5,
         ),
     ),
     "deletion-with-default-backward-backward": (
@@ -407,8 +423,8 @@ def generic_tree_test(
             grand_children_names=["grand-child"],
             node_trials=4,
             children_trials=[6],
-            grand_children_trials=[15],
-            total_trials=4 + 6 + 15,
+            grand_children_trials=[5],
+            total_trials=4 + 6 + 5,
         ),
     ),
     "deletion-without-default-forward-forward": (
@@ -419,10 +435,10 @@ def generic_tree_test(
             experiment_name="grand-child",
             parent_name="child",
             grand_parent_name="root",
-            node_trials=15,
+            node_trials=5,
             parent_trials=6,
             grand_parent_trials=4,
-            total_trials=15,
+            total_trials=5,
         ),
     ),
     "deletion-without-default-forward-backward": (
@@ -435,7 +451,7 @@ def generic_tree_test(
             children_names=["grand-child"],
             node_trials=6,
             parent_trials=4,
-            children_trials=[15],
+            children_trials=[5],
             total_trials=6,
         ),
     ),
@@ -449,7 +465,7 @@ def generic_tree_test(
             grand_children_names=["grand-child"],
             node_trials=4,
             children_trials=[6],
-            grand_children_trials=[15],
+            grand_children_trials=[5],
             total_trials=4,
         ),
     ),
diff --git a/tests/unittests/core/io/test_experiment_builder.py b/tests/unittests/core/io/test_experiment_builder.py
index 3af8ddcc3..5747ae79f 100644
--- a/tests/unittests/core/io/test_experiment_builder.py
+++ b/tests/unittests/core/io/test_experiment_builder.py
@@ -112,7 +112,6 @@ def new_config(random_dt, script_path):
             },
         },
         version=1,
-        pool_size=10,
         max_trials=1000,
         max_broken=5,
         working_dir="",
@@ -192,7 +191,6 @@ def test_get_cmd_config(config_file):
     assert local_config["max_trials"] == 100
     assert local_config["max_broken"] == 5
     assert local_config["name"] == "voila_voici"
-    assert local_config["pool_size"] == 1
     assert local_config["storage"] == {
         "database": {
             "host": "mongodb://user:pass@localhost",
@@ -216,7 +214,6 @@ def test_get_cmd_config_from_incomplete_config(incomplete_config_file):
     assert "algorithms" not in local_config
     assert "max_trials" not in local_config
     assert "max_broken" not in local_config
-    assert "pool_size" not in local_config
     assert "name" not in local_config["storage"]["database"]
     assert (
         local_config["storage"]["database"]["host"] == "mongodb://user:pass@localhost"
@@ -243,7 +240,6 @@ def test_fetch_config_from_db_hit(new_config):
     assert db_config["name"] == new_config["name"]
     assert db_config["refers"] == new_config["refers"]
     assert db_config["metadata"] == new_config["metadata"]
-    assert db_config["pool_size"] == new_config["pool_size"]
     assert db_config["max_trials"] == new_config["max_trials"]
     assert db_config["max_broken"] == new_config["max_broken"]
     assert db_config["algorithms"] == new_config["algorithms"]
@@ -275,7 +271,6 @@ def test_get_from_args_hit(config_file, random_dt, new_config):
     assert exp_view.name == new_config["name"]
     assert exp_view.configuration["refers"] == new_config["refers"]
     assert exp_view.metadata == new_config["metadata"]
-    assert exp_view.pool_size == new_config["pool_size"]
     assert exp_view.max_trials == new_config["max_trials"]
     assert exp_view.max_broken == new_config["max_broken"]
     assert exp_view.algorithms.configuration == new_config["algorithms"]
@@ -299,7 +294,6 @@ def test_get_from_args_hit_no_conf_file(config_file, random_dt, new_config):
     assert exp_view.name == new_config["name"]
     assert exp_view.configuration["refers"] == new_config["refers"]
     assert exp_view.metadata == new_config["metadata"]
-    assert exp_view.pool_size == new_config["pool_size"]
     assert exp_view.max_trials == new_config["max_trials"]
     assert exp_view.max_broken == new_config["max_broken"]
     assert exp_view.algorithms.configuration == new_config["algorithms"]
@@ -333,7 +327,6 @@ def test_build_from_args_no_hit(config_file, random_dt, script_path, new_config)
         assert exp.metadata["user"] == "dendi"
         assert exp.metadata["user_script"] == cmdargs["user_args"][0]
         assert exp.metadata["user_args"] == cmdargs["user_args"]
-        assert exp.pool_size == 1
         assert exp.max_trials == 100
         assert exp.max_broken == 5
         assert exp.algorithms.configuration == {"random": {"seed": None}}
@@ -715,7 +708,6 @@ def test_good_set_before_init_no_hit(self, random_dt, new_config):
         assert exp.name == new_config["name"]
         assert exp.configuration["refers"] == new_config["refers"]
         assert exp.metadata == new_config["metadata"]
-        assert exp.pool_size == new_config["pool_size"]
         assert exp.max_trials == new_config["max_trials"]
         assert exp.max_broken == new_config["max_broken"]
         assert exp.working_dir == new_config["working_dir"]
diff --git a/tests/unittests/core/io/test_resolve_config.py b/tests/unittests/core/io/test_resolve_config.py
index 1aae08ecf..cb8f65630 100644
--- a/tests/unittests/core/io/test_resolve_config.py
+++ b/tests/unittests/core/io/test_resolve_config.py
@@ -113,7 +113,6 @@ def test_fetch_config_from_cmdargs():
         "worker_trials": "worker_trials",
         "exp_max_broken": "exp_max_broken",
         "working_dir": "working_dir",
-        "pool_size": "pool_size",
         "max_trials": "max_trials",
         "heartbeat": "heartbeat",
         "worker_max_trials": "worker_max_trials",
@@ -144,7 +143,6 @@ def test_fetch_config_from_cmdargs():
     assert exp_config.pop("max_trials") == "exp_max_trials"
     assert exp_config.pop("max_broken") == "exp_max_broken"
     assert exp_config.pop("working_dir") == "working_dir"
-    assert exp_config.pop("pool_size") == "pool_size"
 
     assert exp_config == {}
 
@@ -222,7 +220,6 @@ def test_fetch_config(config_file):
         "max_trials": 100,
         "max_broken": 5,
         "name": "voila_voici",
-        "pool_size": 1,
         "algorithms": "random",
         "strategy": "NoParallelStrategy",
     }
@@ -258,7 +255,6 @@ def mocked_config(file_object):
     assert exp_config.pop("max_trials") == orion.core.config.experiment.max_trials
     assert exp_config.pop("max_broken") == orion.core.config.experiment.max_broken
     assert exp_config.pop("working_dir") == orion.core.config.experiment.working_dir
-    assert exp_config.pop("pool_size") == orion.core.config.experiment.pool_size
     assert exp_config.pop("algorithms") == orion.core.config.experiment.algorithms
     assert exp_config.pop("strategy") == orion.core.config.experiment.strategy
 
@@ -267,6 +263,7 @@ def mocked_config(file_object):
     # Test worker subconfig
     worker_config = config.pop("worker")
     assert worker_config.pop("n_workers") == orion.core.config.worker.n_workers
+    assert worker_config.pop("pool_size") == orion.core.config.worker.pool_size
     assert worker_config.pop("executor") == orion.core.config.worker.executor
     assert (
         worker_config.pop("executor_configuration")
diff --git a/tests/unittests/core/worker/test_experiment.py b/tests/unittests/core/worker/test_experiment.py
index 3fc36e9e1..01d3dd0f6 100644
--- a/tests/unittests/core/worker/test_experiment.py
+++ b/tests/unittests/core/worker/test_experiment.py
@@ -46,7 +46,6 @@ def new_config(random_dt):
             },
         },
         version=1,
-        pool_size=10,
         max_trials=1000,
         max_broken=5,
         working_dir=None,
@@ -600,7 +599,6 @@ def test_experiment_pickleable():
     "max_trials",
     "metadata",
     "name",
-    "pool_size",
     "producer",
     "refers",
     "retrieve_result",
diff --git a/tests/unittests/core/worker/test_producer.py b/tests/unittests/core/worker/test_producer.py
index 0e2d349a4..2fd5a8122 100644
--- a/tests/unittests/core/worker/test_producer.py
+++ b/tests/unittests/core/worker/test_producer.py
@@ -58,7 +58,6 @@ def producer(monkeypatch, hacked_exp, random_dt, categorical_values):
     """Return a setup `Producer`."""
     # make init done
 
-    assert hacked_exp.pool_size == 1
     hacked_exp.algorithms.algorithm.possible_values = categorical_values
     hacked_exp.algorithms.seed_rng(0)
     hacked_exp.max_trials = 20
@@ -114,12 +113,11 @@ def test_strategist_observe_completed(producer):
 
 def test_naive_algorithm_is_producing(monkeypatch, producer, random_dt):
     """Verify naive algo is used to produce, not original algo"""
-    producer.experiment.pool_size = 1
     producer.algorithm.algorithm.possible_values = [("gru", "rnn")]
     producer.update()
     monkeypatch.setattr(producer.algorithm.algorithm, "set_state", lambda value: None)
     producer.algorithm.algorithm.possible_values = [("gru", "gru")]
-    producer.produce()
+    producer.produce(1)
 
     assert producer.naive_algorithm.algorithm._num == 1  # pool size
     assert producer.algorithm.algorithm._num == 0
@@ -128,11 +126,10 @@ def test_naive_algorithm_is_producing(monkeypatch, producer, random_dt):
 def test_update_and_produce(producer, random_dt):
     """Test new trials are properly produced"""
     possible_values = [("gru", "rnn")]
-    producer.experiment.pool_size = 1
     producer.experiment.algorithms.algorithm.possible_values = possible_values
 
     producer.update()
-    producer.produce()
+    producer.produce(1)
 
     # Algorithm was ordered to suggest some trials
     num_new_points = producer.naive_algorithm.algorithm._num
@@ -146,11 +143,10 @@ def test_register_new_trials(producer, storage, random_dt):
     trials_in_db_before = len(storage._fetch_trials({}))
     new_trials_in_db_before = len(storage._fetch_trials({"status": "new"}))
 
-    producer.experiment.pool_size = 1
     producer.experiment.algorithms.algorithm.possible_values = [("gru", "rnn")]
 
     producer.update()
-    producer.produce()
+    producer.produce(1)
 
     # Algorithm was ordered to suggest some trials
     num_new_points = producer.naive_algorithm.algorithm._num
@@ -267,7 +263,6 @@ def test_register_duplicate_lies(producer, storage, random_dt):
     producer.strategy._value = 4
 
     # Set specific output value for to algo to ensure successful creation of a new trial.
-    producer.experiment.pool_size = 1
     producer.experiment.algorithms.algorithm.possible_values = [("gru", "rnn")]
 
     producer.update()
@@ -278,7 +273,7 @@ def test_register_duplicate_lies(producer, storage, random_dt):
 
     # Create a new point to make sure additional non-completed trials increase number of lying
     # trials generated
-    producer.produce()
+    producer.produce(1)
 
     trials_non_completed = storage._fetch_trials(query)
     assert len(trials_non_completed) == 5
@@ -362,7 +357,6 @@ def test_naive_algo_trained_on_all_non_completed_trials(producer, storage, rando
 def test_naive_algo_is_discared(producer, monkeypatch):
     """Verify that naive algo is discarded and recopied from original algo"""
     # Set values for predictions
-    producer.experiment.pool_size = 1
     producer.experiment.algorithms.algorithm.possible_values = [("gru", "rnn")]
 
     producer.update()
@@ -373,7 +367,7 @@ def test_naive_algo_is_discared(producer, monkeypatch):
     assert len(producer.algorithm.algorithm._points) == 3
     assert len(first_naive_algorithm.algorithm._points) == (3 + 4)
 
-    producer.produce()
+    producer.produce(1)
 
     # Only update the original algo, naive algo is still not discarded
     update_algorithm(producer)
@@ -392,24 +386,25 @@ def test_concurent_producers(producer, storage, random_dt):
     trials_in_db_before = len(storage._fetch_trials({}))
     new_trials_in_db_before = len(storage._fetch_trials({"status": "new"}))
 
+    # Avoid limiting number of samples from the within the algorithm.
+    producer.algorithm.algorithm.pool_size = 1000
+
     # Set so that first producer's algorithm generate valid point on first time
-    # And second producer produce same point and thus must produce next one two.
+    # And second producer produce same point and thus must produce next one too.
     # Hence, we know that producer algo will have _num == 1 and
     # second producer algo will have _num == 2
     producer.algorithm.algorithm.possible_values = [("gru", "rnn"), ("gru", "gru")]
     # Make sure it starts from index 0
     producer.algorithm.seed_rng(0)
 
-    assert producer.experiment.pool_size == 1
-
     second_producer = Producer(producer.experiment)
     second_producer.algorithm = copy.deepcopy(producer.algorithm)
 
     producer.update()
     second_producer.update()
 
-    producer.produce()
-    second_producer.produce()
+    producer.produce(1)
+    second_producer.produce(2)
 
     # Algorithm was required to suggest some trials
     num_new_points = producer.algorithm.algorithm._num
@@ -438,6 +433,52 @@ def test_concurent_producers(producer, storage, random_dt):
     }
 
 
+def test_concurent_producers_shared_pool(producer, storage, random_dt):
+    """Test concurrent production of new trials share the same pool"""
+    trials_in_db_before = len(storage._fetch_trials({}))
+    new_trials_in_db_before = len(storage._fetch_trials({"status": "new"}))
+
+    # Set so that first producer's algorithm generate valid point on first time
+    # And second producer produce same point and thus must backoff and then stop
+    # because first producer filled the pool.
+    # Hence, we know that producer algo will have _num == 1 and
+    # second producer algo will have _num == 1
+    producer.algorithm.algorithm.possible_values = [("gru", "rnn"), ("gru", "gru")]
+    # Make sure it starts from index 0
+    producer.algorithm.seed_rng(0)
+
+    second_producer = Producer(producer.experiment)
+    second_producer.algorithm = copy.deepcopy(producer.algorithm)
+
+    producer.update()
+    second_producer.update()
+
+    producer.produce(1)
+    second_producer.produce(1)
+
+    # Algorithm was required to suggest some trials
+    num_new_points = producer.algorithm.algorithm._num
+    assert num_new_points == 1  # pool size
+    num_new_points = second_producer.algorithm.algorithm._num
+    assert num_new_points == 0  # pool size
+
+    # `num_new_points` new trials were registered at database
+    assert len(storage._fetch_trials({})) == trials_in_db_before + 1
+    assert len(storage._fetch_trials({"status": "new"})) == new_trials_in_db_before + 1
+    new_trials = list(
+        storage._fetch_trials({"status": "new", "submit_time": random_dt})
+    )
+    assert len(new_trials) == 1
+    assert new_trials[0].experiment == producer.experiment.id
+    assert new_trials[0].start_time is None
+    assert new_trials[0].end_time is None
+    assert new_trials[0].results == []
+    assert new_trials[0].params == {
+        "/decoding_layer": "gru",
+        "/encoding_layer": "rnn",
+    }
+
+
 def test_duplicate_within_pool(producer, storage, random_dt):
     """Test that an algo suggesting multiple points can have a few registered even
     if one of them is a duplicate.
@@ -445,8 +486,8 @@ def test_duplicate_within_pool(producer, storage, random_dt):
     trials_in_db_before = len(storage._fetch_trials({}))
     new_trials_in_db_before = len(storage._fetch_trials({"status": "new"}))
 
-    producer.experiment.pool_size = 2
-    producer.algorithm.algorithm.pool_size = 2
+    # Avoid limiting number of samples from the within the algorithm.
+    producer.algorithm.algorithm.pool_size = 1000
 
     producer.experiment.algorithms.algorithm.possible_values = [
         ("gru", "rnn"),
@@ -455,7 +496,7 @@ def test_duplicate_within_pool(producer, storage, random_dt):
     ]
 
     producer.update()
-    producer.produce()
+    producer.produce(2)
 
     # Algorithm was required to suggest some trials
     num_new_points = producer.algorithm.algorithm._num
@@ -489,8 +530,8 @@ def test_duplicate_within_pool_and_db(producer, storage, random_dt):
     trials_in_db_before = len(storage._fetch_trials({}))
     new_trials_in_db_before = len(storage._fetch_trials({"status": "new"}))
 
-    producer.experiment.pool_size = 2
-    producer.algorithm.algorithm.pool_size = 2
+    # Avoid limiting number of samples from the within the algorithm.
+    producer.algorithm.algorithm.pool_size = 1000
 
     producer.experiment.algorithms.algorithm.possible_values = [
         ("gru", "rnn"),
@@ -499,7 +540,7 @@ def test_duplicate_within_pool_and_db(producer, storage, random_dt):
     ]
 
     producer.update()
-    producer.produce()
+    producer.produce(2)
 
     # Algorithm was required to suggest some trials
     num_new_points = producer.algorithm.algorithm._num
@@ -532,14 +573,12 @@ def test_exceed_max_idle_time_because_of_duplicates(producer, random_dt):
     producer.max_idle_time = timeout  # to limit run-time, default would work as well.
     producer.experiment.algorithms.algorithm.possible_values = [("rnn", "rnn")]
 
-    assert producer.experiment.pool_size == 1
-
     producer.update()
 
     start = time.time()
 
     with pytest.raises(SampleTimeout):
-        producer.produce()
+        producer.produce(1)
 
     assert timeout <= time.time() - start < timeout + 1
 
@@ -558,12 +597,10 @@ def opt_out(self, num=1):
         producer.experiment.algorithms.algorithm.__class__, "suggest", opt_out
     )
 
-    assert producer.experiment.pool_size == 1
-
     producer.update()
 
     with pytest.raises(WaitingForTrials):
-        producer.produce()
+        producer.produce(1)
 
 
 def test_stops_if_algo_done(producer, storage, random_dt, monkeypatch):
@@ -582,12 +619,10 @@ def opt_out_and_complete(self, num=1):
         opt_out_and_complete,
     )
 
-    assert producer.experiment.pool_size == 1
-
     trials_in_db_before = len(storage._fetch_trials({}))
 
     producer.update()
-    producer.produce()
+    producer.produce(1)
 
     assert len(storage._fetch_trials({})) == trials_in_db_before
     assert producer.experiment.algorithms.is_done
@@ -595,14 +630,12 @@ def opt_out_and_complete(self, num=1):
 
 def test_original_seeding(producer):
     """Verify that rng state in original algo changes when duplicate trials is discarded"""
-    assert producer.experiment.pool_size == 1
-
     producer.algorithm.seed_rng(0)
 
     assert producer.algorithm.algorithm._index == 0
 
     producer.update()
-    producer.produce()
+    producer.produce(1)
 
     prev_index = producer.algorithm.algorithm._index
     prev_suggested = producer.algorithm.algorithm._suggested
@@ -616,7 +649,7 @@ def test_original_seeding(producer):
     producer.algorithm.seed_rng(0)
 
     producer.update()
-    producer.produce()
+    producer.produce(1)
 
     assert prev_suggested != producer.algorithm.algorithm._suggested
     assert prev_index < producer.algorithm.algorithm._index
@@ -677,7 +710,7 @@ def suggest(pool_size=None):
 
     producer.update()
     with pytest.raises(SampleTimeout):
-        producer.produce()
+        producer.produce(1)
 
     assert len(new_experiment.fetch_trials(with_evc_tree=False)) == 0
 
@@ -686,7 +719,6 @@ def test_algorithm_is_done(monkeypatch, producer):
     """Verify that producer won't register new samples if algorithm is done meanwhile."""
     producer.experiment.max_trials = 8
     producer.experiment.algorithms.algorithm.max_trials = 8
-    producer.experiment.pool_size = 10
     producer = Producer(producer.experiment)
 
     def suggest_one_only(self, num=1):
@@ -698,12 +730,11 @@ def suggest_one_only(self, num=1):
         producer.experiment.algorithms.algorithm.__class__, "suggest", suggest_one_only
     )
 
-    assert producer.experiment.pool_size == 10
     trials_in_exp_before = len(producer.experiment.fetch_trials())
     assert trials_in_exp_before == producer.experiment.max_trials - 1
 
     producer.update()
-    producer.produce()
+    producer.produce(10)
 
     assert len(producer.experiment.fetch_trials()) == producer.experiment.max_trials
     assert producer.naive_algorithm.is_done
@@ -729,11 +760,13 @@ def suggest_n(self, num):
     # Setup naive algorithm
     producer.update()
 
-    assert len(producer.suggest()) == 3
+    assert len(producer.suggest(50)) == 3
+    # Test pool_size is the min selected
+    assert len(producer.suggest(2)) == 2
     producer.experiment.max_trials = 7
-    assert len(producer.suggest()) == 1
+    assert len(producer.suggest(50)) == 1
     producer.experiment.max_trials = 5
-    assert len(producer.suggest()) == 1
+    assert len(producer.suggest(50)) == 1
 
     trials = producer.experiment.fetch_trials()
     for trial in trials[:4]:
@@ -745,4 +778,6 @@ def suggest_n(self, num):
     producer.update()
 
     # There is now 3 completed and 4 broken. Max trials is 5. Producer should suggest 2
-    assert len(producer.suggest()) == 2
+    assert len(producer.suggest(50)) == 2
+    # Test pool_size is the min selected
+    assert len(producer.suggest(1)) == 1