• Pose selection: due to the high computational cost of exploring all the pose selection methods, we elected to use only the following methods: bestpose, bestpose_GNINA, bestpose_SMINA, bestpose_PLANTS, KORP-PL, ConvexPLR. • 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 (SCORCH, AAScore, NNScore, PLECScore 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 69267 possible final compound rankings for each target in the dataset.