Beyond these implications, the findings in this work also show how ensemble based computational protocols can be used to inform the process of drug discovery. For instance, with respect to ADRP, 4 binding sites that tofacitinib can bind to were identified within both the long-timescale and splitting protocols. From our binding free energy analysis, we identified that tofacitinib binds to the crystallographically determined binding site with the greatest affinity out of each of ADRP the binding sites. This indicates that, in practice, tofacitinib would act as a competitive inhibitor of ADRP. Similarly, various binding sites of interest were identified for other ligand-protein complexes studied with similar conclusions made. In addition, the discovery of non-crystallographically resolved binding sites is of great interest for a future study which would aim to elucidate whether any of these binding sites can propagate allosteric effects to the substrate binding site. This would provide a novel mechanism by which to target the protein and induce anti-viral effects. Finally, we compared the "direct" free energy method with ESMACS and discussed various scenarios where each method has an advantage or limitation. ESMACS is very efficient in ranking ligands based on their binding interactions with much less computational cost as compared to the direct binding affinity method. However, it is subject to the availability of a stable binding pose as the starting structure, in the absence of which long simulations do a better job. We hope that this will help others working in this domain to choose an appropriate free energy method for their purposes.