The degraded density dockings (Fig. ) see additional improvement in the re-docking and cross-docking success rates, especially at 3 Å resolution (4/4, 100%; 18/24, 75% ). Interestingly, the performance for Raltegravir degrades quickly, which we observed to be due to the oxadiazole being positioned next to the nucleotides instead of the fluorphenyl (data not shown). Experimental density-derived guidance improves virtual screening success rates for selected targets. Having shown that our density-derived guidance method improves pose predictions even for ligands not used to create the guidance force, we tested for improvements in our ability to prospectively discriminate between active and inactive compounds during virtual screening. To conduct this validation, we required a high-quality dataset of receptors and known active and inactive/decoy ligands. The Directory of Useful Decoys, Enhanced (DUD-E) is a frequently used dataset for such applications. However, in our initial experiments with the DUD-E we found multiple modeling is-sues in the deposited receptors, including problematic complexes with dimerized ligands (e.g. TRY1: PDB 2AYW ) which made it difficult to validate the quality of the receptor models. Therefore, we modified a different dataset, the LIT-PCBA , for our purposes. This set presents a couple notable advantages, including supplying multiple receptor structures per target and using experimentally-validated inactive compounds instead of decoys predicted in silico.