We compute uncertainties for all metrics through bootstrapping analysis. This method involves resampling with replacement the N input data points (in this case, the replica averages of ∆G MMPBSA ) to provide a new bootstrap sample also containing N data points. This process is repeated many times (in our case 5000 times) and the statistic of interest of each bootstrap population calculated. The standard deviation of these values provides an estimate of the uncertainty associated with an average derived from a given sample, this is what is quoted as the bootstrap error measure of our statistics. For correlation coefficients samples are drawn from the overall averages for each ligand paired with the relevant experimental value. In addition to this metric, when making a direct comparison of specific correlation coefficients we will also quote 95% confidence intervals. These intervals are calculated by sorting the bootstrap sample distribution of correlation coefficients and taking the values falling at the 2.5 and 97.5 percentiles.