diff --git a/ax/models/torch/botorch_modular/acquisition.py b/ax/models/torch/botorch_modular/acquisition.py
index 7581ab9b178..06f27b36f1c 100644
--- a/ax/models/torch/botorch_modular/acquisition.py
+++ b/ax/models/torch/botorch_modular/acquisition.py
@@ -19,7 +19,7 @@
 
 import torch
 from ax.core.search_space import SearchSpaceDigest
-from ax.exceptions.core import AxWarning, SearchSpaceExhausted
+from ax.exceptions.core import AxWarning, SearchSpaceExhausted, UserInputError
 from ax.models.model_utils import enumerate_discrete_combinations, mk_discrete_choices
 from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse
 from ax.models.torch.botorch_modular.surrogate import Surrogate
@@ -41,6 +41,10 @@
 from botorch.acquisition.acquisition import AcquisitionFunction
 from botorch.acquisition.input_constructors import get_acqf_input_constructor
 from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
+from botorch.acquisition.multi_objective.base import (
+    MultiObjectiveAnalyticAcquisitionFunction,
+    MultiObjectiveMCAcquisitionFunction,
+)
 from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
 from botorch.acquisition.risk_measures import RiskMeasureMCObjective
 from botorch.models.model import Model, ModelDict
@@ -101,6 +105,19 @@ def __init__(
         self.surrogates = surrogates
         self.options = options or {}
 
+        if torch_opt_config.is_moo and not issubclass(
+            botorch_acqf_class,
+            (
+                MultiObjectiveAnalyticAcquisitionFunction,
+                MultiObjectiveMCAcquisitionFunction,
+            ),
+        ):
+            raise UserInputError(
+                "Acquisition requires a `MultiObjectiveAnalyticAcquisitionFunction` "
+                "or a `MultiObjectiveMCAcquisitionFunction` class when there are "
+                f"multiple objectives. Received {botorch_acqf_class=}."
+            )
+
         # Compute pending and observed points for each surrogate
         Xs_pending_and_observed = {
             name: _get_X_pending_and_observed(
@@ -215,12 +232,11 @@ def __init__(
             outcome_constraints = torch_opt_config.outcome_constraints
             objective_thresholds = torch_opt_config.objective_thresholds
             subset_idcs = None
-        # If objective weights suggest multiple objectives but objective
-        # thresholds are not specified, infer them using the model that
-        # has already been subset to avoid re-subsetting it within
+        # If MOO and some objective thresholds are not specified, infer them using
+        # the model that has already been subset to avoid re-subsetting it within
         # `inter_objective_thresholds`.
         if (
-            objective_weights.nonzero().numel() > 1
+            torch_opt_config.is_moo
             and (
                 self._objective_thresholds is None
                 or self._objective_thresholds[torch_opt_config.objective_weights != 0]
diff --git a/ax/models/torch/tests/test_acquisition.py b/ax/models/torch/tests/test_acquisition.py
index b9cdd29d31f..1e121c84092 100644
--- a/ax/models/torch/tests/test_acquisition.py
+++ b/ax/models/torch/tests/test_acquisition.py
@@ -18,7 +18,7 @@
 import numpy as np
 import torch
 from ax.core.search_space import SearchSpaceDigest
-from ax.exceptions.core import AxWarning, SearchSpaceExhausted
+from ax.exceptions.core import AxWarning, SearchSpaceExhausted, UserInputError
 from ax.models.torch.botorch_modular.acquisition import Acquisition
 from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse
 from ax.models.torch.botorch_modular.surrogate import Surrogate
@@ -43,6 +43,9 @@
 )
 from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
 from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
+from botorch.acquisition.multi_objective.base import (
+    MultiObjectiveAnalyticAcquisitionFunction,
+)
 from botorch.acquisition.multi_objective.monte_carlo import (
     qNoisyExpectedHypervolumeImprovement,
 )
@@ -89,22 +92,30 @@ def evaluate(self, X: Tensor, **kwargs: Any) -> Tensor:
         return X.sum(dim=-1)
 
 
+class DummyMultiObjectiveAcquisitionFunction(
+    DummyAcquisitionFunction, MultiObjectiveAnalyticAcquisitionFunction
+):
+    # Dummy acquisition function for testing multi-objective setup.
+    ...
+
+
 class AcquisitionTest(TestCase):
     def setUp(self) -> None:
         super().setUp()
         qNEI_input_constructor = get_acqf_input_constructor(qNoisyExpectedImprovement)
+        # Adding wrapping here to be able to count calls and inspect arguments.
         self.mock_input_constructor = mock.MagicMock(
             qNEI_input_constructor, side_effect=qNEI_input_constructor
         )
-        # Adding wrapping here to be able to count calls and inspect arguments.
-        _register_acqf_input_constructor(
-            acqf_cls=DummyAcquisitionFunction,
-            input_constructor=self.mock_input_constructor,
-        )
-        _register_acqf_input_constructor(
-            acqf_cls=DummyOneShotAcquisitionFunction,
-            input_constructor=self.mock_input_constructor,
-        )
+        for acqf_class in (
+            DummyAcquisitionFunction,
+            DummyOneShotAcquisitionFunction,
+            DummyMultiObjectiveAcquisitionFunction,
+        ):
+            _register_acqf_input_constructor(
+                acqf_cls=acqf_class,
+                input_constructor=self.mock_input_constructor,
+            )
         tkwargs: dict[str, Any] = {"dtype": torch.double}
         self.botorch_model_class = SingleTaskGP
         self.surrogate = Surrogate(botorch_model_class=self.botorch_model_class)
@@ -738,7 +749,7 @@ def test_init_moo(
         with_outcome_constraints: bool = True,
     ) -> None:
         acqf_class = (
-            DummyAcquisitionFunction
+            DummyMultiObjectiveAcquisitionFunction
             if with_no_X_observed
             else qNoisyExpectedHypervolumeImprovement
         )
@@ -776,7 +787,19 @@ def test_init_moo(
             objective_weights=moo_objective_weights,
             outcome_constraints=outcome_constraints,
             objective_thresholds=moo_objective_thresholds,
+            is_moo=True,
         )
+        with self.assertRaisesRegex(
+            UserInputError, "when there are multiple objectives"
+        ):
+            Acquisition(
+                surrogates={"surrogate": self.surrogate},
+                botorch_acqf_class=DummyAcquisitionFunction,
+                search_space_digest=self.search_space_digest,
+                torch_opt_config=torch_opt_config,
+                options=self.options,
+            )
+
         acquisition = Acquisition(
             surrogates={"surrogate": self.surrogate},
             botorch_acqf_class=acqf_class,