We demonstrate that using an all-atom molecular mechanics force field combined with an implicit solvent model for scoring protein-ligand complexes is a promising approach for improving inhibitor enrichment in the virtual screening of large compound databases. The rescoring method is evaluated by the extent to which known binders for nine diverse, therapeutically relevant enzymes are enriched against a background of approximately 100,000 drug-like decoys. The improvement in enrichment is most robust and dramatic within the top 1% of the ranked database, that is, the first thousand compounds; below the first few percent of the ranked database, there is little overall improvement. The improved early enrichment is likely due to the more realistic treatment of ligand and receptor desolvation in the rescoring procedure. We also present anecdotal but encouraging results assessing the ability of the rescoring method to predict specificity of inhibitors for structurally related proteins.