diff --git a/deepsensor/data/loader.py b/deepsensor/data/loader.py
index e5d10d2f..06ed6332 100644
--- a/deepsensor/data/loader.py
+++ b/deepsensor/data/loader.py
@@ -1365,9 +1365,11 @@ def sample_variable(var, sampling_strat, seed):
                 rng = np.random.default_rng(split_seed)
 
                 # Keep trying until we get a target set with at least one target point
-                keep_searching = True
-                while keep_searching:
-                    added_mask_date = rng.choice(self.context[context_idx].time)
+                # Iterate through a randomly ordered list of dates
+                added_mask_dates = rng.choice(self.context[context_idx].time, 
+                                              size=len(self.context[context_idx].time),
+                                              replace=False)
+                for i, added_mask_date in enumerate(added_mask_dates):
                     added_mask = (
                         self.context[context_idx].sel(time=added_mask_date).isnull()
                     )
@@ -1379,19 +1381,23 @@ def sample_variable(var, sampling_strat, seed):
                     # TEMP: Inefficient to convert all non-targets to NaNs and then remove NaNs
                     #   when we could just slice the target values here
                     target_mask = added_mask & ~curr_mask
-                    if isinstance(target_var, xr.Dataset):
-                        keep_searching = np.all(target_mask.to_array().data == False)
+                    target_mask_data = target_mask.to_array().data if isinstance(target_var, xr.Dataset) else target_mask.data
+                    # if all elements in target_mask_data are NaN
+                    if np.all(target_mask_data == False):
+                        if i == len(added_mask_dates):
+                            # if the last date in the list has been reached and no suitable mask is found
+                            raise ValueError("")
+                        else:
+                            # otherwise continue on with next random date
+                            continue
                     else:
-                        keep_searching = np.all(target_mask.data == False)
-                    if keep_searching:
-                        continue  # No target points -- use a different `added_mask`
+                        target_var = target_var.where(
+                            target_mask
+                        )  # Only keep target locations
 
-                    target_var = target_var.where(
-                        target_mask
-                    )  # Only keep target locations
-
-                    context_slices[context_idx] = context_var
-                    target_slices[target_idx] = target_var
+                        context_slices[context_idx] = context_var
+                        target_slices[target_idx] = target_var
+                        break
 
         for i, (var, sampling_strat) in enumerate(
             zip(context_slices, context_sampling)