To obtain meaningful estimates of ∆G direct we must take into account the results from all members of an ensemble. To do this, we employ bootstrapping to obtain sampling distributions of the mean for ∆G direct by resampling 5000 times with replacement. The original sample used for such analysis is the ensemble of ∆G direct values from all replicas that sample a given binding site. The probability density functions of the original sample as well as the sampling distributions of means so obtained are displayed in Figure for a selection of systems studied. As we have shown in previous studies for relatively short duration MD trajectories, it is possible that the underlying free energy distributions may be non-Gaussian whereas the corresponding bootstrapped distributions approach the Gaussian functional form with increasing sample size as a consequence of the central limit theorem. Figure provides evidence of similar behaviour in case of "long" MD simulations as well, although given the small sample sizes (as shown in inset) not all bootstrapped distributions are Gaussian either. To be sure, an ensemble of size ≤ 10 is far too small to draw definitive conclusions on the true form of the underlying distribution. To ensure convergence of ∆G direct , it would be necessary to determine the change in the bootstrapped value of ∆G direct as a function of the number of replicas. Upon convergence, the estimate for the binding free energy could be classified as reliable and reproducible.