diff --git a/blackboxopt/visualizations/utils.py b/blackboxopt/visualizations/utils.py
index 6469b898..a69de108 100644
--- a/blackboxopt/visualizations/utils.py
+++ b/blackboxopt/visualizations/utils.py
@@ -173,13 +173,23 @@ def add_plotly_buttons_for_logscale(fig):
 
 
 def mask_pareto_efficient(costs: np.ndarray):
+    """For a given array of objective values where lower values are considered better
+    and the dimensions are samples x objectives, return a mask that is `True` for all
+    pareto efficient values.
+
+    NOTE: The result marks multiple occurrences of the same point all as pareto
+    efficient.
+    """
     is_efficient = np.ones(costs.shape[0], dtype=bool)
     for i, c in enumerate(costs):
-        if is_efficient[i]:
-            is_efficient[is_efficient] = np.any(
-                costs[is_efficient] <= c, axis=1
-            )  # Keep any point with a lower cost
-            is_efficient[i] = True  # And keep self
+        if not is_efficient[i]:
+            continue
+
+        # Keep any point with a lower cost or when they are the same
+        efficient = np.any(costs[is_efficient] < c, axis=1)
+        duplicates = np.all(costs[is_efficient] == c, axis=1)
+        is_efficient[is_efficient] = np.logical_or(efficient, duplicates)
+
     return is_efficient
 
 
diff --git a/pyproject.toml b/pyproject.toml
index adfbeca5..568a07b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "blackboxopt"
-version = "4.3.2"
+version = "4.3.3"
 description = "A common interface for blackbox optimization algorithms along with useful helpers like parallel optimization loops, analysis and visualization scripts."
 readme = "README.md"
 repository = "https://github.com/boschresearch/blackboxopt"
diff --git a/tests/visualization_test.py b/tests/visualization_test.py
index a28f170b..677375a8 100644
--- a/tests/visualization_test.py
+++ b/tests/visualization_test.py
@@ -216,7 +216,6 @@ def test_multi_objective_visualization_without_fidelities():
 
 
 def test_mask_pareto_efficient():
-
     evals = np.array(
         [
             [0.0, 1.0],
@@ -224,15 +223,21 @@ def test_mask_pareto_efficient():
             [0.0, 1.0],
             [1.0, 0.0],
             [3.1, 1.1],
+            [0.1, 1.0],
+            [0.0, 1.1],
+            [-1.0, 2.0],
         ]
     )
     pareto_efficient = mask_pareto_efficient(evals)
-    assert len(pareto_efficient) == 5
+    assert len(pareto_efficient) == evals.shape[0]
     assert pareto_efficient[0]
     assert not pareto_efficient[1]
     assert pareto_efficient[2]
     assert pareto_efficient[3]
     assert not pareto_efficient[4]
+    assert not pareto_efficient[5]
+    assert not pareto_efficient[6]
+    assert pareto_efficient[7]
 
 
 def test_prepare_for_multi_objective_visualization_handles_score_objectives():