diff --git a/tests/unit/acquisition/test_rule.py b/tests/unit/acquisition/test_rule.py
index 1287c70393..b73c1794a8 100644
--- a/tests/unit/acquisition/test_rule.py
+++ b/tests/unit/acquisition/test_rule.py
@@ -16,6 +16,7 @@
 import copy
 from collections.abc import Mapping
 from typing import Callable, Optional
+from unittest.mock import ANY, MagicMock
 
 import gpflow
 import numpy as np
@@ -1798,6 +1799,33 @@ def test_multi_trust_region_box_updated_datasets_are_in_regions(
     )
 
 
+def test_multi_trust_region_box_acquire_filters() -> None:
+    # Create some dummy models and datasets
+    models: Mapping[Tag, ANY] = {"global_tag": MagicMock()}
+    datasets: Mapping[Tag, ANY] = {
+        LocalizedTag("tag1", 1): MagicMock(),
+        LocalizedTag("tag1", 2): MagicMock(),
+        LocalizedTag("tag2", 1): MagicMock(),
+        LocalizedTag("tag2", 2): MagicMock(),
+        "global_tag": MagicMock(),
+    }
+
+    search_space = Box([0.0], [1.0])
+    mock_base_rule = MagicMock(spec=EfficientGlobalOptimization)
+    mock_base_rule.acquire.return_value = tf.constant([[[0.0], [0.0]]], dtype=tf.float64)
+
+    # Create a BatchTrustRegionBox instance with the mock base_rule.
+    subspaces = [SingleObjectiveTrustRegionBox(search_space) for _ in range(2)]
+    rule = BatchTrustRegionBox(subspaces, mock_base_rule)  # type: ignore[var-annotated]
+
+    rule.acquire(search_space, models, datasets)(None)
+
+    # Only the global tags should be passed to the base_rule acquire call.
+    mock_base_rule.acquire.assert_called_once_with(
+        ANY, models, {"global_tag": datasets["global_tag"]}
+    )
+
+
 def test_multi_trust_region_box_state_deepcopy() -> None:
     search_space = Box([0.0, 0.0], [1.0, 1.0])
     dataset = Dataset(
diff --git a/trieste/acquisition/rule.py b/trieste/acquisition/rule.py
index 1ed6353303..fa7b695746 100644
--- a/trieste/acquisition/rule.py
+++ b/trieste/acquisition/rule.py
@@ -1282,7 +1282,19 @@ def state_func(
                     _points.append(rule.acquire(subspace, _models, _datasets))
                 points = tf.stack(_points, axis=1)
             else:
-                points = self._rule.acquire(acquisition_space, models, datasets)
+                # Filter out local datasets as this is the EGO rule with normal acquisition
+                # functions that don't expect local datasets.
+                # Note: no need to filter out local models, as setups with local models
+                # are handled above (i.e. we run the base rule sequentially for each subspace).
+                if datasets is not None:
+                    _datasets = {
+                        tag: dataset
+                        for tag, dataset in datasets.items()
+                        if not LocalizedTag.from_tag(tag).is_local
+                    }
+                else:
+                    _datasets = None
+                points = self._rule.acquire(acquisition_space, models, _datasets)
 
             # We may modify the regions in filter_datasets later, so return a copy.
             state_ = BatchTrustRegion.State(copy.deepcopy(acquisition_space))