diff --git a/baybe/campaign.py b/baybe/campaign.py
index bfa82b3be..8648858f0 100644
--- a/baybe/campaign.py
+++ b/baybe/campaign.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import json
-from typing import List
+from typing import List, Optional
 
 import cattrs
 import numpy as np
@@ -68,13 +68,11 @@ class Campaign(SerialMixin):
     """The number of fits already done."""
 
     # Private
-    _measurements_exp: pd.DataFrame = field(
-        factory=pd.DataFrame, eq=eq_dataframe, init=False
-    )
+    _measurements_exp: pd.DataFrame = field(eq=eq_dataframe, init=False)
     """The experimental representation of the conducted experiments."""
 
-    _cached_recommendation: pd.DataFrame = field(
-        factory=pd.DataFrame, eq=eq_dataframe, init=False
+    _cached_recommendation: Optional[pd.DataFrame] = field(
+        default=None, eq=eq_dataframe, init=False
     )
     """The cached recommendations."""
 
@@ -82,6 +80,13 @@ class Campaign(SerialMixin):
     numerical_measurements_must_be_within_tolerance: bool = field(default=None)
     """Deprecated! Raises an error when used."""
 
+    @_measurements_exp.default
+    def _default_measurements_exp(self):
+        """Provide an empty dataframe with the experimental columns as default."""
+        return pd.DataFrame(
+            columns=[p.name for p in self.parameters] + [t.name for t in self.targets]
+        )
+
     @numerical_measurements_must_be_within_tolerance.validator
     def _validate_tolerance_flag(self, _, value) -> None:
         """Raise a DeprecationError if the tolerance flag is used."""
@@ -110,15 +115,11 @@ def targets(self) -> List[Target]:
     @property
     def _measurements_parameters_comp(self) -> pd.DataFrame:
         """The computational representation of the measured parameters."""
-        if len(self._measurements_exp) < 1:
-            return pd.DataFrame()
         return self.searchspace.transform(self._measurements_exp)
 
     @property
     def _measurements_targets_comp(self) -> pd.DataFrame:
         """The computational representation of the measured targets."""
-        if len(self._measurements_exp) < 1:
-            return pd.DataFrame()
         return self.objective.transform(self._measurements_exp)
 
     @classmethod
@@ -181,7 +182,7 @@ def add_measurements(
             TypeError: If the target has non-numeric entries in the provided dataframe.
         """
         # Invalidate recommendation cache first (in case of uncaught exceptions below)
-        self._cached_recommendation = pd.DataFrame()
+        self._cached_recommendation = None
 
         # Check if all targets have valid values
         for target in self.targets:
@@ -266,7 +267,10 @@ def recommend(
 
         # If there are cached recommendations and the batch size of those is equal to
         # the previously requested one, we just return those
-        if len(self._cached_recommendation) == batch_size:
+        if (
+            self._cached_recommendation is not None
+            and len(self._cached_recommendation) == batch_size
+        ):
             return self._cached_recommendation
 
         # Update recommendation meta data
diff --git a/baybe/searchspace/discrete.py b/baybe/searchspace/discrete.py
index 2d7a45892..efaa202f6 100644
--- a/baybe/searchspace/discrete.py
+++ b/baybe/searchspace/discrete.py
@@ -449,7 +449,7 @@ def transform(
             A dataframe with the parameters in computational representation.
         """
         # If the transformed values are not required, return an empty dataframe
-        if self.empty_encoding or len(data) < 1:
+        if self.empty_encoding:
             comp_rep = pd.DataFrame(index=data.index)
             return comp_rep
 
diff --git a/baybe/surrogates/gaussian_process.py b/baybe/surrogates/gaussian_process.py
index f8835a0e4..fba2bd53a 100644
--- a/baybe/surrogates/gaussian_process.py
+++ b/baybe/surrogates/gaussian_process.py
@@ -147,8 +147,10 @@ def _fit(self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor) -> No
             covar_module=covar_module,
             likelihood=likelihood,
         )
-        mll = ExactMarginalLogLikelihood(self._model.likelihood, self._model)
-        # IMPROVE: The step_limit=100 stems from the former (deprecated)
-        #  `fit_gpytorch_torch` function, for which this was the default. Probably,
-        #   one should use a smarter logic here.
-        fit_gpytorch_mll_torch(mll, step_limit=100)
+
+        if train_x.numel() > 0:
+            mll = ExactMarginalLogLikelihood(self._model.likelihood, self._model)
+            # IMPROVE: The step_limit=100 stems from the former (deprecated)
+            #  `fit_gpytorch_torch` function, for which this was the default. Probably,
+            #   one should use a smarter logic here.
+            fit_gpytorch_mll_torch(mll, step_limit=100)
diff --git a/baybe/surrogates/utils.py b/baybe/surrogates/utils.py
index d109d9cb4..09067f991 100644
--- a/baybe/surrogates/utils.py
+++ b/baybe/surrogates/utils.py
@@ -28,12 +28,7 @@ def _prepare_inputs(x: Tensor) -> Tensor:
 
     Returns:
         The prepared input.
-
-    Raises:
-        ValueError: If the model input is empty.
     """
-    if len(x) == 0:
-        raise ValueError("The model input must be non-empty.")
     return x.to(_DTYPE)
 
 
diff --git a/tests/test_iterations.py b/tests/test_iterations.py
index 36243f06e..d8b9fa7de 100644
--- a/tests/test_iterations.py
+++ b/tests/test_iterations.py
@@ -148,3 +148,9 @@ def test_iter_recommender_hybrid(campaign, n_iterations, batch_size):
 @pytest.mark.parametrize("strategy", valid_strategies, indirect=True)
 def test_strategies(campaign, n_iterations, batch_size):
     run_iterations(campaign, n_iterations, batch_size)
+
+
+@pytest.mark.parametrize("parameter_names", [["Num_disc_1", "Conti_finite3"]])
+def test_without_data(campaign, batch_size):
+    campaign.strategy = SequentialGreedyRecommender()
+    campaign.recommend(batch_size)