To better separate pose prediction from scoring, our two groups decided to collaborate and combine our different areas of expertise in this study -docking and free energy calculations and participate in the structure-based binding affinity prediction challenge for the target BACE-1 in D3R GC4. We used AutoDock-GPU for docking the ligands, employing the AutoDock4 scoring function, and then calculated MM-GBSA binding affinities. MM-GBSA binding energy calculations did not improve the predictive performance with respect to AutoDock4 scores. In a retrospective analysis, we identified two modeling aspects that were detrimental to the quality of MM-GBSA scores, namely the choice of protein conformation and the protonation state of residues in the binding pocket. While it is clear that MM-GBSA can make better predictions than docking scores, making the optimal modeling choices is a non-trivial task that requires knowledge of the system under study and thus is likely difficult in a prospective setting. Thus, our assessment of MM-GBSA performance roughly agrees with that of several previous research groups which participated in previous D3R Grand Challenges specifically, we find that MM-GBSA does not perform par-ticularly well at binding affinity estimation. These resultsnot just our own -highlight the importance of blind challenges for the community to evaluate method performance, particularly for drug design projects.