Further complexity in the identification of chemical matter that is active on P-gp arises from the pharmacological outcome that small molecules can show, ranging between inhibitor binders, substrates binders, and non-binders. To address such challenges, we implemented and tested the performance of a consensus scoring scheme designed using molecules with available experimental data on this target, including all three pharmacological classes. As reported by others, consensus scoring approaches are known to improve the overall performance of virtual screenings, enriching results and hit rates on a variety of targets . On one hand, a direct structure-based VS approach was able to efficiently differentiate between P-gp binders and non-binders using molecular docking alone, according to the binders' enrichment factor obtained on Validation set 1 and Validation set 2. On the other hand, the promiscuity between substrate binders and inhibitor binders posed a significant challenge, which we addressed by combining the molecular docking results with pharmacophore modeling. This approach led to the identification of 14 potential P-gp inhibitors from a library of about 197000 commercially available natural compounds. Among them, 13 were tested in different in vitro models, and 9 compounds were found to have P-gp inhibitory activity. These results show that the combination of different in silico approaches can be beneficial to address challenging discrimination tasks where individual methods are not sufficient. The experimental validation proven the method successful at identifying novel natural products as scaffolds for designing optimized P-gp binders capable of binding with the desired pharmacological effect. This will contribute expanding the current therapeutic strategies to overcome P-gp-mediated multidrug resistance and to enhance the bioavailability of drugs and natural products that are known to be P-gp substrates. Additionally, this approach demonstrates that with the opportune training data, the combination of multiple modeling techniques can increase the resolution power of virtual screenings to go beyond the simple binder/non-binder classification.