• Pose selection: due to the high computational cost of exploring each scoring function as a pose selection tool, we elected to use only the following methods: RMSD, spyRMSD, espsim, 3DScore, bestpose, bestpose_GNINA, bestpose_SMINA, bestpose_PLANTS, KORP-PL, ConvexPLR and RTMScore. • Pose busting: Due to the high computational cost of using Pose Busters on this many docking poses, we elected to not enable this feature. • Rescoring: GNINA-Affinity, CNN-Score, CNN-Affinity, Vinardo, AD4, KORP-PL, ConvexPLR, LinF9, RTMScore, RFScoreVS, CHEMPLP, NNScore, PLECScore (SCORCH and AAScore were omitted in the benchmarking used due to high computational cost. PLP was omitted due to high correlation with CHEMPLP). • Consensus: for benchmarking purposes, every consensus method available in DockM8 was used. Moreover, the consensus was considered for every pose selection method and every combination of the selected scoring functions. This represents 556179 possible final compound rankings for each target in the dataset.