Ligands from a set of 19 protein-ligand complexes were re-docked with AutoDock, GOLD and FlexX using the scoring algorithms native to these programs supplemented by analysis using the HINT free energy force field. A HINT scoring function was calibrated for this data set using a simple linear regression of total HINT score for crystal-structure complexes vs. measured free energy of binding. This function had an r(2) of 0.84 and a standard error of +/-0.42 kcal mol(-1). The free energies of binding were calculated for the best poses using the AutoDock, GOLD and FlexX scoring functions. The AutoDock and GoldScore algorithms estimated more than half of the binding free energies within the reported calibration standard errors for these functions, while that of FlexX did not. In contrast, the calibrated HINT scoring function identified optimized poses with standard errors near +/-0.5 kcal mol(-1). When the metric of success is minimum RMSD (vs. crystallographic coordinates) the three docking programs were more successful, with mean RMSDs for the top-ranking poses in the 19 complexes of 3.38, 2.52 and 2.62 A for AutoDock, GOLD and FlexX, respectively. Two key observations in this study have general relevance for computational medicinal chemistry: first, while optimizing RMSD with docking score functions is clearly of value, these functions may be less well optimized for free energy of binding, which has broader applicability in virtual screening and drug discovery than RMSD; second, scoring functions uniquely calibrated for the data set or sets under study should nearly always be preferable to universal scoring functions. Due to these advantages, the poses selected by the HINT score also required less post-docking structure optimization to produce usable molecular models. Most of these features may be achievable with other scoring functions.