The results obtained for this dataset are consistent with those obtained using Validation set 1, both in terms of binding energies (Fig. ) and ligand efficiency (Fig. ), with similar trends for the average binding energy and ligand efficiency of binders, (lower and higher, respectively) compared to the non-binders. High correlation between ranking and docking score is found (Fig. ). The quartile ranking of compounds, based on the binding energy, shows that there is an increasing probability of finding a binder moving from the last to the first quartile. An inverse trend can be observed for non-binders, which are much more represented in the last quartile. Focusing on the first quartile, although binders are homogeneously distributed, the probability of finding non-binders continues to decrease while moving to the very first section of the ranking (Fig. ). Based on these results, we argued that this docking protocol could be successfully used to enrich the top ranked results of a VS for the identification of P-gp binders. In absence of evidence of binding of multiple copies of known P-gp binders other than zosuquidar and considering the good enrichment of binders/non-binders found with the single ligand docking, we choose not to use the multiple ligands docking protocol for the VS of the Screening set. Moreover, filtering the VS results by selecting only those with a binding energy lower than the average obtained with active compounds (-8.5 kcal/mol) could represent a strategy for reducing the number of predicted hits to be evaluated in the subsequent steps of the analysis. However, neither the binding energy nor the ligand efficiency were able to discern between substrates and inhibitors (Fig. and), suggesting that more accurate analyses are needed for this challenging task. To improve the ability to differentiate between inhibitors and substrates, we used docking poses to generate 3D common pharmacophore models of selected inhibitors and substrates. Table reports the main features of the generated pharmacophores. On average, 3D pharmacophoric models of substrates show a higher number of hydrogen bond acceptor, aliphatic carbon, and halogen features. On the contrary, more aromatic and sulfur features (i.e.: hydrophobic) were found in the 3D pharmacophore models of inhibitors. No significant difference was found in the number of hydrogen bond donor features.