diff --git a/baybe/campaign.py b/baybe/campaign.py
index e7e93839f..233d62312 100644
--- a/baybe/campaign.py
+++ b/baybe/campaign.py
@@ -29,6 +29,7 @@
     telemetry_record_value,
 )
 from baybe.utils.boolean import eq_dataframe
+from baybe.utils.plotting import to_string
 
 
 @define
@@ -84,19 +85,14 @@ class Campaign(SerialMixin):
     """The cached recommendations."""
 
     def __str__(self) -> str:
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-
-        # Get str representation of campaign fields
-        fields_to_print = [self.searchspace, self.objective, self.recommender]
-        fields_str = "\n\n".join(str(x) for x in fields_to_print)
-
-        # Put all relevant attributes of the campaign in one string
-        campaign_str = f"""{start_bold}Campaign{end_bold}
-        \n{start_bold}Meta Data{end_bold}\nBatches Done: {self.n_batches_done}
-        \rFits Done: {self.n_fits_done}\n\n{fields_str}\n"""
-
-        return campaign_str.replace("\n", "\n ").replace("\r", "\r ")
+        metadata_fields = [
+            to_string("Batches done", self.n_batches_done, single_line=True),
+            to_string("Fits done", self.n_fits_done, single_line=True),
+        ]
+        metadata = to_string("Meta Data", *metadata_fields)
+        fields = [metadata, self.searchspace, self.objective, self.recommender]
+
+        return to_string(self.__class__.__name__, *fields)
 
     @property
     def measurements(self) -> pd.DataFrame:
diff --git a/baybe/objectives/desirability.py b/baybe/objectives/desirability.py
index 1346a6f4e..5aac1103f 100644
--- a/baybe/objectives/desirability.py
+++ b/baybe/objectives/desirability.py
@@ -16,7 +16,9 @@
 from baybe.targets.base import Target
 from baybe.targets.numerical import NumericalTarget
 from baybe.utils.basic import to_tuple
+from baybe.utils.dataframe import pretty_print_df
 from baybe.utils.numerical import geom_mean
+from baybe.utils.plotting import to_string
 from baybe.utils.validation import finite_float
 
 
@@ -122,19 +124,17 @@ def _normalized_weights(self) -> np.ndarray:
         return np.asarray(self.weights) / np.sum(self.weights)
 
     def __str__(self) -> str:
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-
         targets_list = [target.summary() for target in self.targets]
         targets_df = pd.DataFrame(targets_list)
         targets_df["Weight"] = self.weights
 
-        objective_str = f"""{start_bold}Objective{end_bold}
-        \n{start_bold}Type: {end_bold}{self.__class__.__name__}
-        \n{start_bold}Targets {end_bold}\n{targets_df}
-        \n{start_bold}Scalarizer: {end_bold}{self.scalarizer.name}"""
+        fields = [
+            to_string("Type", self.__class__.__name__, single_line=True),
+            to_string("Targets", pretty_print_df(targets_df)),
+            to_string("Scalarizer", self.scalarizer.name, single_line=True),
+        ]
 
-        return objective_str.replace("\n", "\n ")
+        return to_string("Objective", *fields)
 
     def transform(self, data: pd.DataFrame) -> pd.DataFrame:  # noqa: D102
         # See base class.
diff --git a/baybe/objectives/single.py b/baybe/objectives/single.py
index 2e91f2343..59f322279 100644
--- a/baybe/objectives/single.py
+++ b/baybe/objectives/single.py
@@ -6,6 +6,8 @@
 
 from baybe.objectives.base import Objective
 from baybe.targets.base import Target
+from baybe.utils.dataframe import pretty_print_df
+from baybe.utils.plotting import to_string
 
 
 @define(frozen=True, slots=False)
@@ -16,17 +18,15 @@ class SingleTargetObjective(Objective):
     """The single target considered by the objective."""
 
     def __str__(self) -> str:
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-
         targets_list = [target.summary() for target in self.targets]
         targets_df = pd.DataFrame(targets_list)
 
-        objective_str = f"""{start_bold}Objective{end_bold}
-        \n{start_bold}Type: {end_bold}{self.__class__.__name__}
-        \n{start_bold}Targets {end_bold}\n{targets_df}"""
+        fields = [
+            to_string("Type", self.__class__.__name__, single_line=True),
+            to_string("Targets", pretty_print_df(targets_df)),
+        ]
 
-        return objective_str.replace("\n", "\n ")
+        return to_string("Objective", *fields)
 
     @property
     def targets(self) -> tuple[Target, ...]:  # noqa: D102
diff --git a/baybe/parameters/numerical.py b/baybe/parameters/numerical.py
index 5c6c34117..950821c98 100644
--- a/baybe/parameters/numerical.py
+++ b/baybe/parameters/numerical.py
@@ -18,7 +18,7 @@
 
 @define(frozen=True, slots=False)
 class NumericalDiscreteParameter(DiscreteParameter):
-    """Parameter class for discrete numerical parameters (a.k.a. setpoints)."""
+    """Class for discrete numerical parameters (a.k.a. setpoints)."""
 
     # class variables
     is_numerical: ClassVar[bool] = True
@@ -100,7 +100,7 @@ def is_in_range(self, item: float) -> bool:  # noqa: D102
 
 @define(frozen=True, slots=False)
 class NumericalContinuousParameter(ContinuousParameter):
-    """Parameter class for continuous numerical parameters."""
+    """Class for continuous numerical parameters."""
 
     # class variables
     is_numerical: ClassVar[bool] = True
diff --git a/baybe/recommenders/meta/sequential.py b/baybe/recommenders/meta/sequential.py
index d7d48e5e0..6c38596ff 100644
--- a/baybe/recommenders/meta/sequential.py
+++ b/baybe/recommenders/meta/sequential.py
@@ -22,6 +22,7 @@
     block_serialization_hook,
     converter,
 )
+from baybe.utils.plotting import to_string
 
 
 @define
@@ -64,6 +65,14 @@ def select_recommender(  # noqa: D102
             else self.initial_recommender
         )
 
+    def __str__(self) -> str:
+        fields = [
+            to_string("Initial recommender", self.initial_recommender),
+            to_string("Recommender", self.recommender),
+            to_string("Switch after", self.switch_after, single_line=True),
+        ]
+        return to_string(self.__class__.__name__, *fields)
+
 
 @define
 class SequentialMetaRecommender(MetaRecommender):
@@ -164,6 +173,13 @@ def select_recommender(  # noqa: D102
 
         return recommender
 
+    def __str__(self) -> str:
+        fields = [
+            to_string("Recommenders", self.recommenders),
+            to_string("Mode", self.mode, single_line=True),
+        ]
+        return to_string(self.__class__.__name__, *fields)
+
 
 @define
 class StreamingSequentialMetaRecommender(MetaRecommender):
@@ -242,6 +258,12 @@ def select_recommender(  # noqa: D102
 
         return self._last_recommender  # type: ignore[return-value]
 
+    def __str__(self) -> str:
+        fields = [
+            to_string("Recommenders", self.recommenders),
+        ]
+        return to_string(self.__class__.__name__, *fields)
+
 
 # The recommender iterable cannot be serialized
 converter.register_unstructure_hook(
diff --git a/baybe/recommenders/pure/bayesian/botorch.py b/baybe/recommenders/pure/bayesian/botorch.py
index d5fc36420..2ec317dce 100644
--- a/baybe/recommenders/pure/bayesian/botorch.py
+++ b/baybe/recommenders/pure/bayesian/botorch.py
@@ -17,6 +17,7 @@
     SubspaceDiscrete,
 )
 from baybe.utils.dataframe import to_tensor
+from baybe.utils.plotting import to_string
 from baybe.utils.sampling_algorithms import (
     DiscreteSamplingMethod,
     sample_numerical_df,
@@ -295,3 +296,20 @@ def _recommend_hybrid(
         rec_exp = pd.concat([rec_disc_exp, rec_cont_exp], axis=1)
 
         return rec_exp
+
+    def __str__(self) -> str:
+        fields = [
+            to_string("Surrogate", self.surrogate_model),
+            to_string(
+                "Acquisition function", self.acquisition_function, single_line=True
+            ),
+            to_string("Compatibility", self.compatibility, single_line=True),
+            to_string(
+                "Sequential continuous", self.sequential_continuous, single_line=True
+            ),
+            to_string("Hybrid sampler", self.hybrid_sampler, single_line=True),
+            to_string(
+                "Sampling percentage", self.sampling_percentage, single_line=True
+            ),
+        ]
+        return to_string(self.__class__.__name__, *fields)
diff --git a/baybe/recommenders/pure/nonpredictive/clustering.py b/baybe/recommenders/pure/nonpredictive/clustering.py
index 425d9202b..70d228496 100644
--- a/baybe/recommenders/pure/nonpredictive/clustering.py
+++ b/baybe/recommenders/pure/nonpredictive/clustering.py
@@ -13,6 +13,7 @@
 
 from baybe.recommenders.pure.nonpredictive.base import NonPredictiveRecommender
 from baybe.searchspace import SearchSpaceType, SubspaceDiscrete
+from baybe.utils.plotting import to_string
 
 
 @define
@@ -125,6 +126,18 @@ def _recommend_discrete(
         # Convert positional indices into DataFrame indices and return result
         return candidates_comp.index[selection]
 
+    def __str__(self) -> str:
+        fields = [
+            to_string("Compatibility", self.compatibility, single_line=True),
+            to_string(
+                "Name of clustering parameter",
+                self.model_cluster_num_parameter_name,
+                single_line=True,
+            ),
+            to_string("Model parameters", self.model_params, single_line=True),
+        ]
+        return to_string(self.__class__.__name__, *fields)
+
 
 @define
 class PAMClusteringRecommender(SKLearnClusteringRecommender):
diff --git a/baybe/recommenders/pure/nonpredictive/sampling.py b/baybe/recommenders/pure/nonpredictive/sampling.py
index dd8a1a73f..3811e65f1 100644
--- a/baybe/recommenders/pure/nonpredictive/sampling.py
+++ b/baybe/recommenders/pure/nonpredictive/sampling.py
@@ -8,6 +8,7 @@
 
 from baybe.recommenders.pure.nonpredictive.base import NonPredictiveRecommender
 from baybe.searchspace import SearchSpace, SearchSpaceType, SubspaceDiscrete
+from baybe.utils.plotting import to_string
 from baybe.utils.sampling_algorithms import farthest_point_sampling
 
 
@@ -45,6 +46,10 @@ def _recommend_hybrid(
         cont_random.index = disc_random.index
         return pd.concat([disc_random, cont_random], axis=1)
 
+    def __str__(self) -> str:
+        fields = [to_string("Compatibility", self.compatibility, single_line=True)]
+        return to_string(self.__class__.__name__, *fields)
+
 
 class FPSRecommender(NonPredictiveRecommender):
     """An initial recommender that selects candidates via Farthest Point Sampling."""
@@ -70,3 +75,7 @@ def _recommend_discrete(
         candidates_scaled = np.ascontiguousarray(scaler.transform(candidates_comp))
         ilocs = farthest_point_sampling(candidates_scaled, batch_size)
         return candidates_comp.index[ilocs]
+
+    def __str__(self) -> str:
+        fields = [to_string("Compatibility", self.compatibility, single_line=True)]
+        return to_string(self.__class__.__name__, *fields)
diff --git a/baybe/searchspace/continuous.py b/baybe/searchspace/continuous.py
index 7b898810a..5e4438397 100644
--- a/baybe/searchspace/continuous.py
+++ b/baybe/searchspace/continuous.py
@@ -30,6 +30,7 @@
 from baybe.serialization import SerialMixin, converter, select_constructor_hook
 from baybe.utils.basic import to_tuple
 from baybe.utils.dataframe import pretty_print_df
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     from baybe.searchspace.core import SearchSpace
@@ -71,9 +72,6 @@ def __str__(self) -> str:
         if self.is_empty:
             return ""
 
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-
         # Convert the lists to dataFrames to be able to use pretty_printing
         param_list = [param.summary() for param in self.parameters]
         eq_constraints_list = [constr.summary() for constr in self.constraints_lin_eq]
@@ -84,21 +82,18 @@ def __str__(self) -> str:
             constr.summary() for constr in self.constraints_nonlin
         ]
         param_df = pd.DataFrame(param_list)
-        lin_eq_constr_df = pd.DataFrame(eq_constraints_list)
-        lin_ineq_constr_df = pd.DataFrame(ineq_constraints_list)
-        nonlinear_constr_df = pd.DataFrame(nonlin_constraints_list)
-
-        # Put all attributes of the continuous class in one string
-        continuous_str = f"""{start_bold}Continuous Search Space{end_bold}
-            \n{start_bold}Continuous Parameters{end_bold}\n{pretty_print_df(param_df)}
-            \n{start_bold}List of Linear Equality Constraints{end_bold}
-            \r{pretty_print_df(lin_eq_constr_df)}
-            \n{start_bold}List of Linear Inequality Constraints{end_bold}
-            \r{pretty_print_df(lin_ineq_constr_df)}
-            \n{start_bold}List of Nonlinear Constraints{end_bold}
-            \r{pretty_print_df(nonlinear_constr_df)}"""
-
-        return continuous_str.replace("\n", "\n ").replace("\r", "\r ")
+        lin_eq_df = pd.DataFrame(eq_constraints_list)
+        lin_ineq_df = pd.DataFrame(ineq_constraints_list)
+        nonlinear_df = pd.DataFrame(nonlin_constraints_list)
+
+        fields = [
+            to_string("Continuous Parameters", pretty_print_df(param_df)),
+            to_string("Linear Equality Constraints", pretty_print_df(lin_eq_df)),
+            to_string("Linear Inequality Constraints", pretty_print_df(lin_ineq_df)),
+            to_string("Non-linear Constraints", pretty_print_df(nonlinear_df)),
+        ]
+
+        return to_string(self.__class__.__name__, *fields)
 
     @property
     def constraints_cardinality(self) -> tuple[ContinuousCardinalityConstraint, ...]:
diff --git a/baybe/searchspace/core.py b/baybe/searchspace/core.py
index 23af0188f..55c0b6f4c 100644
--- a/baybe/searchspace/core.py
+++ b/baybe/searchspace/core.py
@@ -25,6 +25,7 @@
 from baybe.searchspace.validation import validate_parameters
 from baybe.serialization import SerialMixin, converter, select_constructor_hook
 from baybe.telemetry import TELEM_LABELS, telemetry_record_value
+from baybe.utils.plotting import to_string
 
 
 class SearchSpaceType(Enum):
@@ -66,19 +67,14 @@ class SearchSpace(SerialMixin):
     """The (potentially empty) continuous subspace of the overall search space."""
 
     def __str__(self) -> str:
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-        head_str = f"""{start_bold}Search Space{end_bold}
-        \n{start_bold}Search Space Type: {end_bold}{self.type.name}"""
-
-        # Check the sub space size to avoid adding unwanted break lines
-        # if the sub space is empty
-        discrete_str = f"\n\n{self.discrete}" if not self.discrete.is_empty else ""
-        continuous_str = (
-            f"\n\n{self.continuous}" if not self.continuous.is_empty else ""
-        )
-        searchspace_str = f"{head_str}{discrete_str}{continuous_str}"
-        return searchspace_str.replace("\n", "\n ").replace("\r", "\r ")
+        fields = [
+            to_string("Search Space Type", self.type.name, single_line=True),
+        ]
+        if not self.discrete.is_empty:
+            fields.append(str(self.discrete))
+        if not self.continuous.is_empty:
+            fields.append(str(self.continuous))
+        return to_string(self.__class__.__name__, *fields)
 
     def __attrs_post_init__(self):
         """Perform validation and record telemetry values."""
diff --git a/baybe/searchspace/discrete.py b/baybe/searchspace/discrete.py
index 41797702e..27bf049a5 100644
--- a/baybe/searchspace/discrete.py
+++ b/baybe/searchspace/discrete.py
@@ -39,6 +39,7 @@
 )
 from baybe.utils.memory import bytes_to_human_readable
 from baybe.utils.numerical import DTypeFloatNumpy
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     import polars as pl
@@ -120,9 +121,6 @@ def __str__(self) -> str:
         if self.is_empty:
             return ""
 
-        start_bold = "\033[1m"
-        end_bold = "\033[0m"
-
         # Convert the lists to dataFrames to be able to use pretty_printing
         param_list = [param.summary() for param in self.parameters]
         constraints_list = [constr.summary() for constr in self.constraints]
@@ -135,19 +133,31 @@ def __str__(self) -> str:
         dont_recommend_count = len(self.metadata[self.metadata[_METADATA_COLUMNS[2]]])
         metadata_count = len(self.metadata)
 
-        # Put all attributes of the discrete class in one string.
-        discrete_str = f"""{start_bold}Discrete Search Space{end_bold}
-            \n{start_bold}Discrete Parameters{end_bold}\n{pretty_print_df(param_df)}
-            \n{start_bold}Experimental Representation{end_bold}
-            \r{pretty_print_df(self.exp_rep)}\n\n{start_bold}Metadata:{end_bold}
-            \r{_METADATA_COLUMNS[0]}: {was_recommended_count}/{metadata_count}
-            \r{_METADATA_COLUMNS[1]}: {was_measured_count}/{metadata_count}
-            \r{_METADATA_COLUMNS[2]}: {dont_recommend_count}/{metadata_count}
-            \n{start_bold}Constraints{end_bold}\n{pretty_print_df(constraints_df)}
-            \n{start_bold}Computational Representation{end_bold}
-            \r{pretty_print_df(self.comp_rep)}"""
-
-        return discrete_str.replace("\n", "\n ").replace("\r", "\r ")
+        metadata_fields = [
+            to_string(
+                f"{_METADATA_COLUMNS[0]}",
+                f"{was_recommended_count}/{metadata_count}",
+                single_line=True,
+            ),
+            to_string(
+                f"{_METADATA_COLUMNS[1]}",
+                f"{was_measured_count}/{metadata_count}",
+                single_line=True,
+            ),
+            to_string(
+                f"{_METADATA_COLUMNS[2]}",
+                f"{dont_recommend_count}/{metadata_count}",
+                single_line=True,
+            ),
+        ]
+        fields = [
+            to_string("Discrete Parameters", pretty_print_df(param_df)),
+            to_string("Experimental Representation", pretty_print_df(self.exp_rep)),
+            to_string("Meta Data", *metadata_fields),
+            to_string("Constraints", pretty_print_df(constraints_df)),
+            to_string("Computational Representation", pretty_print_df(self.comp_rep)),
+        ]
+        return to_string(self.__class__.__name__, *fields)
 
     @exp_rep.validator
     def _validate_exp_rep(  # noqa: DOC101, DOC103
diff --git a/baybe/surrogates/bandit.py b/baybe/surrogates/bandit.py
index a90e64b2b..cf8c47291 100644
--- a/baybe/surrogates/bandit.py
+++ b/baybe/surrogates/bandit.py
@@ -13,6 +13,7 @@
 from baybe.searchspace.core import SearchSpace
 from baybe.surrogates.base import Surrogate
 from baybe.targets.binary import _FAILURE_VALUE_COMP, _SUCCESS_VALUE_COMP
+from baybe.utils.plotting import to_string
 from baybe.utils.random import temporary_seed
 
 if TYPE_CHECKING:
@@ -160,3 +161,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor, _: Any = None) -> None:
         wins = (train_x * (train_y == float(_SUCCESS_VALUE_COMP))).sum(dim=0)
         losses = (train_x * (train_y == float(_FAILURE_VALUE_COMP))).sum(dim=0)
         self._win_lose_counts = torch.vstack([wins, losses]).to(torch.int)
+
+    def __str__(self) -> str:
+        fields = [to_string("Prior", self.prior, single_line=True)]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/surrogates/base.py b/baybe/surrogates/base.py
index 680333185..6f891faba 100644
--- a/baybe/surrogates/base.py
+++ b/baybe/surrogates/base.py
@@ -28,6 +28,7 @@
 )
 from baybe.serialization.mixin import SerialMixin
 from baybe.utils.dataframe import to_tensor
+from baybe.utils.plotting import to_string
 from baybe.utils.scaling import ColumnTransformer
 
 if TYPE_CHECKING:
@@ -312,6 +313,16 @@ def fit(
     def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         """Perform the actual fitting logic."""
 
+    def __str__(self) -> str:
+        fields = [
+            to_string(
+                "Supports Transfer Learning",
+                self.supports_transfer_learning,
+                single_line=True,
+            ),
+        ]
+        return to_string(self.__class__.__name__, *fields)
+
 
 @define
 class IndependentGaussianSurrogate(Surrogate, ABC):
diff --git a/baybe/surrogates/custom.py b/baybe/surrogates/custom.py
index c3054c3d9..0153d3ad5 100644
--- a/baybe/surrogates/custom.py
+++ b/baybe/surrogates/custom.py
@@ -27,6 +27,7 @@
 from baybe.surrogates.base import IndependentGaussianSurrogate
 from baybe.surrogates.utils import batchify_mean_var_prediction
 from baybe.utils.numerical import DTypeFloatONNX
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     import onnxruntime as ort
@@ -137,3 +138,7 @@ def validate_compatibility(cls, searchspace: SearchSpace) -> None:
                 f"a one-dimensional computational representation or "
                 f"{CustomDiscreteParameter.__name__}."
             )
+
+    def __str__(self) -> str:
+        fields = [to_string("ONNX input name", self.onnx_input_name, single_line=True)]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/surrogates/gaussian_process/core.py b/baybe/surrogates/gaussian_process/core.py
index 2778e85a7..89a759090 100644
--- a/baybe/surrogates/gaussian_process/core.py
+++ b/baybe/surrogates/gaussian_process/core.py
@@ -22,6 +22,7 @@
     DefaultKernelFactory,
     _default_noise_factory,
 )
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     from botorch.models.model import Model
@@ -211,3 +212,9 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
             botorch.optim.fit.fit_gpytorch_mll_torch(mll, step_limit=200)
         else:
             botorch.fit.fit_gpytorch_mll(mll)
+
+    def __str__(self) -> str:
+        fields = [
+            to_string("Kernel factory", self.kernel_factory, single_line=True),
+        ]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/surrogates/linear.py b/baybe/surrogates/linear.py
index b0d5f9c36..a4196f04c 100644
--- a/baybe/surrogates/linear.py
+++ b/baybe/surrogates/linear.py
@@ -10,6 +10,7 @@
 from baybe.surrogates.base import IndependentGaussianSurrogate
 from baybe.surrogates.utils import batchify_mean_var_prediction, catch_constant_targets
 from baybe.surrogates.validation import get_model_params_validator
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     from torch import Tensor
@@ -58,3 +59,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         # See base class.
         self._model = ARDRegression(**(self.model_params))
         self._model.fit(train_x, train_y.ravel())
+
+    def __str__(self) -> str:
+        fields = [to_string("Model Params", self.model_params, single_line=True)]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/surrogates/ngboost.py b/baybe/surrogates/ngboost.py
index 7f4a7a1a3..6a79fb345 100644
--- a/baybe/surrogates/ngboost.py
+++ b/baybe/surrogates/ngboost.py
@@ -11,6 +11,7 @@
 from baybe.surrogates.base import IndependentGaussianSurrogate
 from baybe.surrogates.utils import batchify_mean_var_prediction, catch_constant_targets
 from baybe.surrogates.validation import get_model_params_validator
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     from botorch.models.transforms.input import InputTransform
@@ -82,3 +83,7 @@ def _estimate_moments(
     def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         # See base class.
         self._model = NGBRegressor(**(self.model_params)).fit(train_x, train_y.ravel())
+
+    def __str__(self) -> str:
+        fields = [to_string("Model Params", self.model_params, single_line=True)]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/surrogates/random_forest.py b/baybe/surrogates/random_forest.py
index 2db112b99..570feecbc 100644
--- a/baybe/surrogates/random_forest.py
+++ b/baybe/surrogates/random_forest.py
@@ -13,6 +13,7 @@
 from baybe.surrogates.base import Surrogate
 from baybe.surrogates.utils import batchify_ensemble_predictor, catch_constant_targets
 from baybe.surrogates.validation import get_model_params_validator
+from baybe.utils.plotting import to_string
 
 if TYPE_CHECKING:
     from botorch.models.ensemble import EnsemblePosterior
@@ -104,3 +105,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         # See base class.
         self._model = RandomForestRegressor(**(self.model_params))
         self._model.fit(train_x.numpy(), train_y.numpy().ravel())
+
+    def __str__(self) -> str:
+        fields = [to_string("Model Params", self.model_params, single_line=True)]
+        return to_string(super().__str__(), *fields)
diff --git a/baybe/utils/dataframe.py b/baybe/utils/dataframe.py
index 7eec8c38d..80d3c1c4a 100644
--- a/baybe/utils/dataframe.py
+++ b/baybe/utils/dataframe.py
@@ -449,7 +449,13 @@ def fuzzy_row_match(
     return pd.Index(inds_matched)
 
 
-def pretty_print_df(df: pd.DataFrame, max_rows: int = 6, max_columns: int = 4) -> str:
+def pretty_print_df(
+    df: pd.DataFrame,
+    max_rows: int = 6,
+    max_columns: int = 4,
+    max_colwidth: int = 16,
+    precision: int = 3,
+) -> str:
     """Convert a dataframe into a pretty/readable format.
 
     This function returns a customized str representation of the dataframe.
@@ -459,6 +465,8 @@ def pretty_print_df(df: pd.DataFrame, max_rows: int = 6, max_columns: int = 4) -
         df: The dataframe to be printed.
         max_rows: Maximum number of rows to display.
         max_columns: Maximum number of columns to display.
+        max_colwidth: Maximum width of an individual column.
+        precision: Number of digits to which numbers should be rounded.
 
     Returns:
         The values to be printed as a str table.
@@ -469,8 +477,19 @@ def pretty_print_df(df: pd.DataFrame, max_rows: int = 6, max_columns: int = 4) -
         max_rows,
         "display.max_columns",
         max_columns,
+        "display.max_colwidth",
+        max_colwidth,
+        "display.precision",
+        precision,
         "expand_frame_repr",
         False,
     ):
-        str_df = str(df)
+        # Pandas does not truncate the names of columns with long names, which makes
+        # computational representations barely readable in some of the examples. Hence,
+        # we truncate them manually. For details, see
+        # https://stackoverflow.com/questions/64976267/pandas-truncate-column-names)
+        str_df = df.rename(
+            columns=lambda x: x[:max_colwidth],
+        )
+        str_df = str(str_df)
     return str_df
diff --git a/baybe/utils/plotting.py b/baybe/utils/plotting.py
index 9a8697665..a73ea6b50 100644
--- a/baybe/utils/plotting.py
+++ b/baybe/utils/plotting.py
@@ -140,3 +140,37 @@ def create_example_plots(
         else:
             warnings.warn("Plots could not be saved.")
     plt.close()
+
+
+def indent(text: str, amount: int = 3, ch: str = " ") -> str:
+    """Indent a given text by a certain amount."""
+    padding = amount * ch
+    return "".join(padding + line for line in text.splitlines(keepends=True))
+
+
+def to_string(header: str, *fields: Any, single_line: bool = False) -> str:
+    """Create a nested string representation.
+
+    Args:
+        header: The header, typically the name of a class.
+        *fields: Fields to be printed with an indentation.
+        single_line: If ``True``, print the representation on a single line.
+            Only applicable when given a single field.
+
+    Raises:
+        ValueError: If ``single_line`` is ``True`` but ``fields`` contains more than one
+            element.
+
+    Returns:
+        The string representation with indented fields.
+    """
+    if single_line:
+        if len(fields) > 1:
+            raise ValueError(
+                "``single_line`` is only applicable when given a single field."
+            )
+        # Since single line headers look ugly without a ":", we add it manually
+        header = header if header.endswith(":") else header + ":"
+        return f"{header} {str(fields[0])}"
+
+    return "\n".join([header] + [indent(str(f)) for f in fields])
diff --git a/examples/Basics/recommenders.py b/examples/Basics/recommenders.py
index 3d541a1ad..4759041df 100644
--- a/examples/Basics/recommenders.py
+++ b/examples/Basics/recommenders.py
@@ -45,6 +45,7 @@
 
 
 # Per default the initial recommender chosen is a random recommender.
+
 INITIAL_RECOMMENDER = RandomRecommender()
 
 ### Available surrogate models
@@ -52,8 +53,10 @@
 # This model uses available data to model the objective function as well as the uncertainty.
 # The surrogate model is then used by the acquisition function to make recommendations.
 
-# The following are the available basic surrogates
-print(get_subclasses(Surrogate))
+# The following are the available basic surrogates:
+
+for subclass in get_subclasses(Surrogate):
+    print(subclass)
 
 # Per default a Gaussian Process is used
 # You can change the used kernel by using the optional `kernel` keyword.
diff --git a/examples/Constraints_Continuous/hybrid_space.py b/examples/Constraints_Continuous/hybrid_space.py
index 7cde1128c..ea6d26c3a 100644
--- a/examples/Constraints_Continuous/hybrid_space.py
+++ b/examples/Constraints_Continuous/hybrid_space.py
@@ -74,8 +74,8 @@
 ]
 
 # We model the following constraints:
-# `1.0*x_1 + 1.0*x_2 = 1.0`
-# `1.0*x_3 - 1.0*x_4 = 2.0`
+# - $1.0*x_1 + 1.0*x_2 = 1.0$
+# - $1.0*x_3 - 1.0*x_4 = 2.0$
 
 constraints = [
     DiscreteSumConstraint(
@@ -118,7 +118,7 @@
 measurements = campaign.measurements
 TOLERANCE = 0.01
 
-# `1.0*x_1 + 1.0*x_2 = 1.0`
+# $1.0*x_1 + 1.0*x_2 = 1.0$
 
 print(
     "1.0*x_1 + 1.0*x_2 = 1.0 satisfied in all recommendations? ",
@@ -127,7 +127,7 @@
     ),
 )
 
-# `1.0*x_3 - 1.0*x_4 = 2.0`
+# $1.0*x_3 - 1.0*x_4 = 2.0$
 
 print(
     "1.0*x_3 - 1.0*x_4 = 2.0 satisfied in all recommendations? ",
diff --git a/examples/Constraints_Continuous/linear_constraints.py b/examples/Constraints_Continuous/linear_constraints.py
index def3bc6d9..74934276c 100644
--- a/examples/Constraints_Continuous/linear_constraints.py
+++ b/examples/Constraints_Continuous/linear_constraints.py
@@ -58,10 +58,10 @@
 ]
 
 # We model the following constraints:
-# `1.0*x_1 + 1.0*x_2 = 1.0`
-# `1.0*x_3 - 1.0*x_4 = 2.0`
-# `1.0*x_1 + 1.0*x_3 >= 1.0`
-# `2.0*x_2 + 3.0*x_4 <= 1.0` which is equivalent to `-2.0*x_2 - 3.0*x_4 >= -1.0`
+# - $1.0*x_1 + 1.0*x_2 = 1.0$
+# - $1.0*x_3 - 1.0*x_4 = 2.0$
+# - $1.0*x_1 + 1.0*x_3 >= 1.0$
+# - $2.0*x_2 + 3.0*x_4 <= 1.0$ which is equivalent to $-2.0*x_2 - 3.0*x_4 >= -1.0$
 
 constraints = [
     ContinuousLinearEqualityConstraint(
@@ -112,7 +112,7 @@
 measurements = campaign.measurements
 TOLERANCE = 0.01
 
-# `1.0*x_1 + 1.0*x_2 = 1.0`
+# $1.0*x_1 + 1.0*x_2 = 1.0$
 
 print(
     "1.0*x_1 + 1.0*x_2 = 1.0 satisfied in all recommendations? ",
@@ -121,7 +121,7 @@
     ),
 )
 
-# `1.0*x_3 - 1.0*x_4 = 2.0`
+# $1.0*x_3 - 1.0*x_4 = 2.0$
 
 print(
     "1.0*x_3 - 1.0*x_4 = 2.0 satisfied in all recommendations? ",
@@ -130,14 +130,14 @@
     ),
 )
 
-# `1.0*x_1 + 1.0*x_3 >= 1.0`
+# $1.0*x_1 + 1.0*x_3 >= 1.0$
 
 print(
     "1.0*x_1 + 1.0*x_3 >= 1.0 satisfied in all recommendations? ",
     (1.0 * measurements["x_1"] + 1.0 * measurements["x_3"]).ge(1.0 - TOLERANCE).all(),
 )
 
-# `2.0*x_2 + 3.0*x_4 <= 1.0`
+# $2.0*x_2 + 3.0*x_4 <= 1.0$
 
 print(
     "2.0*x_2 + 3.0*x_4 <= 1.0 satisfied in all recommendations? ",
diff --git a/examples/Searchspaces/continuous_space_botorch_function.py b/examples/Searchspaces/continuous_space_botorch_function.py
index 46b7f48db..aa7bc7246 100644
--- a/examples/Searchspaces/continuous_space_botorch_function.py
+++ b/examples/Searchspaces/continuous_space_botorch_function.py
@@ -49,8 +49,8 @@
 
 ### Creating the searchspace and the objective
 
-# Since the searchspace is continuous test, we construct `NumericalContinuousParameter`s
-# We use that data of the test function to deduce bounds and number of parameters.
+# Since the searchspace is continuous, we use `NumericalContinuousParameter`s.
+# We use the data of the test function to deduce bounds and number of parameters.
 
 parameters = [
     NumericalContinuousParameter(
diff --git a/examples/Serialization/basic_serialization.py b/examples/Serialization/basic_serialization.py
index 72f8caeb1..4f4aadac5 100644
--- a/examples/Serialization/basic_serialization.py
+++ b/examples/Serialization/basic_serialization.py
@@ -76,6 +76,7 @@
 print(campaign_recreate, end="\n" * 3)
 
 # Verify that both objects are equal.
+
 assert campaign == campaign_recreate
 print("Passed basic assertion check!")
 
diff --git a/examples/Serialization/create_from_config.py b/examples/Serialization/create_from_config.py
index f11b336ea..9ca83a117 100644
--- a/examples/Serialization/create_from_config.py
+++ b/examples/Serialization/create_from_config.py
@@ -59,14 +59,13 @@
         "constraints": []
     },
     "objective": {
-        "mode": "SINGLE",
-        "targets": [
+        "type": "SingleTargetObjective",
+        "target":
             {
                 "type": "NumericalTarget",
                 "name": "Yield",
                 "mode": "MAX"
             }
-        ]
     },
     "recommender": {
         "type": "TwoPhaseMetaRecommender",
@@ -96,5 +95,6 @@
 campaign = Campaign.from_config(CONFIG)
 
 # We now perform a recommendation as usual and print it.
+
 recommendation = campaign.recommend(batch_size=3)
 print(recommendation)
diff --git a/examples/Serialization/validate_config.py b/examples/Serialization/validate_config.py
index 52e8bd311..c6aa37252 100644
--- a/examples/Serialization/validate_config.py
+++ b/examples/Serialization/validate_config.py
@@ -58,14 +58,13 @@
         "constraints": []
     },
     "objective": {
-        "mode": "SINGLE",
-        "targets": [
+        "type": "SingleTargetObjective",
+        "target":
             {
                 "type": "NumericalTarget",
                 "name": "Yield",
                 "mode": "MAX"
             }
-        ]
     },
     "recommender": {
         "type": "TwoPhaseMetaRecommender",
@@ -129,14 +128,13 @@
         "constraints": []
     },
     "objective": {
-        "mode": "SINGLE",
-        "targets": [
+        "type": "SingleTargetObjective",
+        "target":
             {
                 "type": "NumericalTarget",
                 "name": "Yield",
                 "mode": "MAX"
             }
-        ]
     },
     "recommender": {
         "type": "TwoPhaseMetaRecommender",