Our main goal in this work was to see whether re-scoring docked poses with MM-GBSA scores can improve the correlation of predicted binding affinities with experimental values. We used AutoDock4.2 scores and MM-GBSA scores to rank the binding affinities of BACE-1 ligands in Stage 2 in D3R GC4. Our workflow consists of a series of entirely automated steps, starting with a SMILES representation of the ligands up to the MM-GBSA score. For this reason, the results reported herein are representative of automated approaches. The performance of docking and MM-GBSA scores in predicting the affinities of the 154 ligands is assessed by the Kendall's τ and Spearman's ρ rank correlation coefficients. We also report Pearson's r and R 2 . We have compiled all these metrics for our predictions in Table . Since all metrics lead to the same conclusions, we base our discussion on Kendall's τ values, which is the first metric reported in the D3R evaluation page. Re-scoring docked poses with MM-GBSA did not improve correlation with experimental values. The Kendall's τ between experimental pK d and AutoDock4.2 scores (submission cq7ug) is 0.19 ± 0.06, and that of MM-GBSA scores (submission utgv6) is 0.20 ± 0.06. Thus, the predictive performance of these methods is statistically identical and both of them correlate poorly with the experimental values. Nevertheless, our predictions are statistically better than a random prediction which has an average Kendall tau of zero. In the context of all submissions to Stage 2 of GC4, our predictions ranked in the top third (Fig. ).