Ultra-large chemical libraries have grown to tens of billions of molecules, requiring new, more efficient screening methods. Previous methods, such as active learning, approached this problem by quickly approximating docking scores, avoiding the need to prepare molecules and perform expensive explicit docking calculations. Despite the speed of these approximations, active learning requires multiple rounds of inference on the entire chemical library, which becomes infeasible at large library sizes. Furthermore, current active learning workflows must be trained for each protein target, as the ML models are protein-specific.