where ℓ i is the pose selected for the current ligand, S is our set of similarities, f (s(•, •)| Native) and f (s(•, •)| Reference) refer to the pre-computed native and reference similarity distributions, respectively, and C is a hyperparameter to weight the CNNscore. We attempt to identify the set of selected poses that optimizes this objective function through a greedy iterative process. We continue iterating through the ligands, updating the selected pose, until no new poses are selected for any ligands. This procedure is run 500 times to increase the likelihood of finding the global minimum. The poses selected by the objective function for each ligand are returned.