Overlap coefficients furnish an incomplete picture as they do not quantify the actual distances between the two distributions. In addition, they do not always capture the existence of outliers in one or both distributions. It may be possible that, despite a zero overlap, the distributions are very close to each other and hence the predicted ∆G may not be too biased. Therefore, we define another metric named "distance" which is the difference between the inner extremes of the two work distributions. In other words, it is the difference between the smallest value of the distribution towards the right (forward We note that there is a handful of cases with negative distance values despite zero overlap. These correspond to situations where both distributions have such outliers that fall within the range of values (extended due to the presence of outlier(s)) of the other distribution. There may also be cases where outliers exist in one of the distributions leading to artificially reduced distance values. All these factors reduce the correlation between UE and distances and can only be addressed properly on performing ensemble simulations.