diff --git a/recommendations/tests/test_optimisers.py b/recommendations/tests/test_optimisers.py
index d1d1b55f..825d28c0 100644
--- a/recommendations/tests/test_optimisers.py
+++ b/recommendations/tests/test_optimisers.py
@@ -779,12 +779,24 @@ def _filter_measures_by_types(input_measures, allowed_types):
     return filtered
 
 
-def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, social=False):
+def _is_eligible_funding_package(scheme, starting_sap, total_gain):
+    if scheme == "eco4":
+        # We check if we meet the upgrade requirements
+        # If the property is an E or above, we need to upgrade to a C or above
+        if starting_sap >= 39:  # ie. EPC C or above
+            return starting_sap + total_gain >= 69
+
+    if scheme == "gbis":
+        # GBIS is a fixed measure only, so we don't check the gain
+        return True
+
+
+def optimise_with_funding_paths(p, input_measures, housing_type, budget=None, target_gain=None):
     """
     run_optimizer(sub_measures, budget, target_gain) -> (picked_options, sub_cost, sub_gain)
     """
 
-    funding_paths = make_funding_paths(p, input_measures, body.housing_type)
+    funding_paths = make_funding_paths(p, input_measures, housing_type)
 
     # We now produce a fabric only path for ECO4
     # We add in generic insulation funding paths (where there is no fixed measure)
@@ -811,6 +823,8 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
             if picked is None:
                 continue
 
+            scheme = _path_scheme([path_spec])
+
             solutions.append(
                 {
                     "fixed_ids": [],
@@ -818,8 +832,11 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
                     "total_cost": sub_cost,
                     "total_gain": sub_gain,
                     "path": path_spec,
+                    "scheme": scheme,
+                    "is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], sub_gain)
                 }
             )
+
             continue
 
         # 1) expand fixed selections for this path
@@ -872,12 +889,16 @@ def optimise_with_funding_paths(input_measures, budget=None, target_gain=None, s
             total_gain = fixed_gain + sub_gain
             total_picks = fixed_items + picked
 
+            scheme = _path_scheme(path_spec)
+
             solutions.append({
                 "fixed_ids": fixed_ids,
                 "items": total_picks,
                 "total_cost": total_cost,
                 "total_gain": total_gain,
                 "path": path_spec,
+                "scheme": scheme,
+                "is_eligible": _is_eligible_funding_package(scheme, p.data["current-energy-efficiency"], sub_gain)
             })
 
     solutions = pd.DataFrame(solutions)