A concise summary of the results obtained for the entire dataset as well as for each protein system individually is shown in Table and Figure . The mean unsigned error (MUE) and Pearson's correlation coefficient (r p ) for all 503 predictions when compared with corresponding experimental values are 1.04 ± 0.04 kcal/mol and 0.58 ± 0.03, respectively. For individual protein systems, these values vary from 0.58 ± 0.18 kcal/mol to 1.41 ± 0.13 kcal/mol and from 0.24 ± 0.23 to 0.88 ± 0.04 for MUE and r p , respectively. Around 32%, 59%, 77% and 87% of our predictions differ from the corresponding experimental ∆∆Gs by less than 0.5 kcal/mol, 1 kcal/mol, 1.5 kcal/mol and 2 kcal/mol, respectively. On the other hand, only 19 (∼4%) have experimental values differing by more than 3 kcal/mol, that is which fall outside the shaded regions in Figure . It is worth mentioning here that all these results have been obtained by a simple application of the standard TIES protocol. As we have mentioned, the TIES protocol is flexible, and hence the accuracy and/or precision of results can be improved by adjusting it in specific cases. We will show in the following sections that, on adapting the standard TIES protocol in various ways for different systems, one may further improve the reliability of the results yielding more robust ∆∆G predictions. We compared RBFEs from FEP and TIES obtained using three different MD engines and found that our results were in agreement only when using ensemble simulations. This is not the case for results based on one-off simulations. Therefore, we expect to get the same results from ensemble based FEP for this dataset. Gapsys et al. recently reported the performance of a couple of other alchemical free energy methods (the non-equilibrium PMX and the proprietary shrink-wrapped FEP+) using most of this dataset. A meaningful comparison of results from different methods and/or forcefields is only possible when uncertainties associated with such calculations are under control.