Accordingly, we used the algorithm in three modes: no protein flexibility (no energy minimization performed), with semi-flexible protein (final energy minimization only), and with a fully flexible protein (iterative energy minimization performed). The RMSDs for all poses generated from all 'Top Percent' values and all flexibility modes are calculated with respect to the experimental crystal pose using a symmetry independent method. Finally, we wished to determine the best scoring function to select the crystal pose from all generated poses (the 'selector' scoring function') and potentially differentiate it from the scoring function used to rank the activity of a given ligand to the protein target of interest (the 'ranker' scoring function). To do this, we calculated the score of all poses generated for the PDBbind core set using all scoring functions mentioned in section 2.1. We then evaluated the ability of each scoring function to select the crystal pose of a ligand from all poses as well as the correlation between the score assigned to the selected pose and the experimental binding affinity. As there are 96 scoring functions, there are 9216 (96 ways to select*96 ways to rank) different methods to rank the affinity of the ligands in the PDBbind core set. An overview of this benchmarking process for activity prediction is given in Figure .