diff --git a/ax/models/torch/botorch_modular/acquisition.py b/ax/models/torch/botorch_modular/acquisition.py
index 06da10c7701..f3b7aa8fcde 100644
--- a/ax/models/torch/botorch_modular/acquisition.py
+++ b/ax/models/torch/botorch_modular/acquisition.py
@@ -543,6 +543,8 @@ def optimize(
             return candidates, acqf_values, arm_weights
 
         # 2b. Handle mixed search spaces that have discrete and continuous features.
+        # Only sequential optimization is supported for `optimize_acqf_mixed`.
+        optimizer_options_with_defaults.pop("sequential")
         candidates, acqf_values = optimize_acqf_mixed(
             acq_function=self.acqf,
             bounds=bounds,
diff --git a/ax/models/torch/tests/test_acquisition.py b/ax/models/torch/tests/test_acquisition.py
index 7dc4a716e51..3fed96edb87 100644
--- a/ax/models/torch/tests/test_acquisition.py
+++ b/ax/models/torch/tests/test_acquisition.py
@@ -510,9 +510,7 @@ def test_optimize_acqf_discrete_local_search(
         tkwargs = {"dtype": self.X.dtype, "device": self.X.device}
         ssd = SearchSpaceDigest(
             feature_names=["a", "b", "c"],
-            # pyre-fixme[6]: For 2nd param expected `List[Tuple[Union[float, int],
-            #  Union[float, int]]]` but got `List[Tuple[int, int, int]]`.
-            bounds=[(0, 0, 0), (1, 1, 1)],
+            bounds=[(0, 1) for _ in range(3)],
             categorical_features=[0, 1, 2],
             discrete_choices={  # 30 * 60 * 90 > 100,000
                 k: np.linspace(0, 1, 30 * (k + 1)).tolist() for k in range(3)
@@ -568,7 +566,6 @@ def test_optimize_mixed(self, mock_optimize_acqf_mixed: Mock) -> None:
         )
         mock_optimize_acqf_mixed.assert_called_with(
             acq_function=acquisition.acqf,
-            sequential=True,
             bounds=mock.ANY,
             q=3,
             options={"init_batch_limit": 32, "batch_limit": 5},