Hydrophobic contact similarity and MCSS RMSD are sufficient features to increase the pose prediction performance to that of the full Open-ComBind pipeline. This is likely due to the fact that hydrophobic contacts are the most common intramolecular interaction seen in the benchmark dataset by a factor of 6 (Table ??). Only utilizing these two features significantly decreases the computational cost of Open-ComBind. In fact, with congeneric series, we see (Figure ) that only MCSS is required for the same performance as the full Open-ComBind pipeline. We expect congeneric series to have a much higher consistency in the placement of the MCSS because the MCSS is likely referring to the scaffold of the congeneric series. We also see that only 10-15 helper ligands are required to reach the pose prediction power of 20 helper ligands (Figures and). Reducing the number of helper ligands will also decrease the computational cost of the docking pipeline as a similarity matrix must be constructed for each feature which is O(N 2 ) with the number of ligands used, N .