We use the same evaluation metrics as in McNutt et al. . After training the CNN models, we employ them in the GNINA molecular docking pipeline with default settings. We set the random seed to ensure consistency between runs of a given complex. We utilize TopN to evaluate the pose ranking performance of the molecular docking pipeline. TopN indicates the percentage of complexes with at least one pose, ranked N or higher, less than 2 Å RMSD to ground truth. That is, Top2 is the percentage of docking runs where at least one of the top two ranked poses is less than 2 Å RMSD from the ground truth. We also investigate the computational cost of using these CNN models by evaluating the time to dock ligands. Since our focus is on increasing the efficiency of CNNs for high-throughput molecular docking, we examine the computational cost of the CNNs during CPU-only docking. GPUs can significantly speed up CNN usage; however, docking millions of molecules generally requires parallelizing the process across hundreds to thousands of machines which is prohibitively expensive if GPUs are used. Therefore, we report the Avg Time Per System, the average CPU-only docking time across the 100 complex subset of the PDBbind Core set v2016. More information on the calculation of these metrics is provided in the supplement.