In a recent D3R challenge, which consisted of predicting binding modes of HSP90 ligands, different docking studies had varying levels of accuracyeven within submissions using the same docking software-but human screening of the structures seemed to help identify the correct binding mode. Four of the 11 top scoring methods used visual inspection of the computationally predicted poses, while the less successful methods did not, indicating how it remains extremely challenging to pre-dict binding modes. Another study by Warren et al. looked at how well different docking programs performed across a variety of different protein targets. They found that docking methods could explore the conformational space of the ligand sufficiently, but the top scoring pose often did not correspond to the observed crystallographic pose. In fact, humans tend to outperform automated methods at predicting binding modes in blind challenges, further showing that there are still many aspects of binding mode prediction that could benefit from improvement. In the SAMPL4 HIV integrase challenge, for example, determining the binding mode or even binding site of a set of ligands to HIV integrase was shown to be very difficult for many methods. A human expert with more than 10 years working on the target provided the best submission, in large part guided by his expertise. The best purely computational method in this challenge used docking followed by free energy calculations to predict whether compounds would bind to HIV integrase. In this study, the majority of false negative binding results used starting poses vastly dissimilar from crystallographic observations, indicating that many of the failures resulted from incorrect binding modes.