diff --git a/pdf_agents/scientific_value.py b/pdf_agents/scientific_value.py
index 64e669c..a394fd5 100644
--- a/pdf_agents/scientific_value.py
+++ b/pdf_agents/scientific_value.py
@@ -8,11 +8,12 @@
 from botorch import fit_gpytorch_mll
 from botorch.acquisition import UpperConfidenceBound, qUpperConfidenceBound
 from botorch.models import SingleTaskGP
-from botorch.optim import optimize_acqf
+from botorch.optim import optimize_acqf  # noqa: F401
 from gpytorch.mlls import ExactMarginalLogLikelihood
 from scipy.spatial import distance_matrix
 
 from .agents import PDFBaseAgent
+from .utils import make_wafer_grid_list
 
 logger = getLogger("pdf_agents.scientific_value")
 
@@ -140,9 +141,19 @@ def ask(self, batch_size: int = 1):
             if batch_size == 1
             else qUpperConfidenceBound(gp, beta=self.ucb_beta).to(self.device)
         )
-        candidates, acq_value = optimize_acqf(
-            acq, bounds=self.bounds, q=batch_size, num_restarts=self.num_restarts, raw_samples=self.raw_samples
-        )
+
+        # Override the optimization to use a grid search over a circular wafer
+        # candidates, acq_value = optimize_acqf(
+        #     acq, bounds=self.bounds, q=batch_size, num_restarts=self.num_restarts, raw_samples=self.raw_samples
+        # )
+        grid = torch.tensor(make_wafer_grid_list(*self.bounds.cpu().numpy().ravel(), step=self.motor_resolution))[
+            :, None, :
+        ]
+        acq_grid = acq(grid)
+        top_indicies = torch.argsort(acq_grid, descending=True, dim=0)[:batch_size]
+        candidates = grid[top_indicies].squeeze(1)
+        acq_value = acq_grid[top_indicies]
+
         if batch_size == 1:
             acq_value = [acq_value]