Furthermore, it is not uncommon for specific λ-windows of a given alchemical transformation to experience larger autocorrelation times than other λ-windows, resulting in slower convergence, fewer uncorrelated samples, and greater statistical uncertainty. In contrast, other λ-windows converge very quickly, with additional sampling affording marginal at best accuracy benefits. In short, using a uniform allocation of resources may result in wasting resources simulating already converged λ-windows, while starving more difficult λ-windows of resources. This issue is magnified when considering a high-throughput computational drug discovery campaign due to the large number of necessary simulations. Below, we propose an algorithm to address this problem in an automatic and entirely data-driven manner without a priori information on the system in question.