In case of the NEQ method, it is necessary (but not sufficient) for the distributions of work values in forward and reverse directions to have non-negligible overlap with each other in order to obtain reliable ABFE predictions . In cases of poor or no overlap, the predictions are at best biased but most likely incorrect, hence highly unreliable. Thus, it is important to check the extent of overlap between the two distributions and ensure that it is non-zero. None of the published articles reporting the applications of NEQ approaches for ABFE predictions even discuss this issue, let alone quantify the extent of such overlap. In the case of RBFEs, Procacci discussed this matter briefly , and more recently, we discussed it in greater detail . For ABFEs, Gapsys et al. suggested a metric to check the overlap but used only a small dataset of 7 protein-ligand systems. Here, we define a quantity termed the "overlap coefficient" which is defined as the area of intersection of the two work distributions. A value of zero indicates that there is no overlap between the two distributions, whereas a value of 1 denotes identical distributions in both directions. We calculate overlap coefficients for each ABFE prediction in this study and systematically investigate the factors affecting its value.