The failure of the active learning methods to perform better than random molecule selection when we restricted all the data to one cluster suggests that there might not be signal present in the data. In order to test this, we need some baseline of maximal performance against which to compare. In order to calculate this baseline we exhaustively train a new model for every molecule in the withheld set during each step of the active learning loop. We then greedily select the molecule which resulted in the model that had the lowest RMSE on the test set. This greedy selection will have the best possible active learning ordering for whatever test set we are evaluating.