The crucial idea here is that to even begin to generate reproducible estimates for ∆G direct , running ensembles of simulations, irrespective of their length, is an imperative. Interestingly, Figure : Probability density functions of ∆G direct values using 5 bins as well as sampling distributions of mean direct free energies ( ∆G direct ) obtained with bootstrapping (5000 resamples) for four of the systems studied at their respective crystallographic binding sites. Bar plots display density histograms of the set of final ∆G direct values from all replicas that sample the respective bindings sites which constitutes the original sample used for bootstrapping whereas solid lines represent kernel density estimations of corresponding sampling distributions of mean obtained using bootstrapping. Sizes of the original samples are shown in the text boxes within each plot. The data suggests that there may be non-Gaussian behaviour in the underlying distribution. The x-axis is expressed in kcal/mol. the non-normal nature of free energy distributions implies more frequent occurrence of outliers than would be expected with normal distributions that necessitates relatively more data in order to obtain reliable estimates. Since the variability that exists across replicas within an ensemble of simulations is caused by the intrinsically chaotic nature of MD simulations, these principles will apply to the calculation of any MD derived macroscopic expectation value.