In this study, we have used a dataset of 219 complexes comprising 31 different proteins and 186 compounds with a variety of scaffolds -the largest dataset used so far for validating an alchemical ABFE methodology. Given the substantial differences between the two end-states in an absolute alchemical binding free energy calculation, it is important to control the uncertainties associated with such calculations to ensure reliable and reproducible results . We have previously demonstrated that the RBFE results obtained using different molecular dynamics engines (NAMD vs OpenMM) and different methods (TI vs FEP) agree with each other when using ensemble simulations but not when using one-off simulations . Molecular dynamics (MD) is choatic; hence performing ensemble simulations is a necessity not an option if robust and reliable predictions are sought . This is true irrespective of the temporal duration of the MD simulation; see for example our recent study using simulations of duration up to 10 µs where divergence becomes very substantial indeed . There are several published studies demonstrating that, given a fixed amount of simulation time, ensembles of shorter simulations perform a better sampling than one-off (or a handful of) relatively longer simulations . The theoretical reason is the mixing nature of the dynamics, owing which each replica rapidly decorrelates from all others, while a single trajectory behaves in the opposite manner, each time step being closely correlated with the preceding one . Therefore, we apply our TIES approach based on ensemble simulations in this study to capture the distributions of predicted binding affinities and perform rigorous uncertainty quantification to ensure reliable results. We obtain free energies by performing ensemble averaging over replicas as stipulated by the principles of statistical mechanics.