The active learning loop is as follows: 1) Train a model from scratch using the available training data and log its performance on the test set, 2) Using the trained model, predict the labels of every molecule in the withheld set, 3) Select the molecule to be added to the training set via a selection criteria, 4) Repeat steps 1-3 until there are no molecules remaining in the withheld set. We investigated 3 strategies for selecting the molecule to add to the training set. In order of increasing complexity we have: 1) Highest variance between the predicted labels of a 5 model ensemble, 2) Highest predicted variance of a single model regressing to a Gaussian distribution, and 3) highest predicted evidence of a single model regressing to a NIG distribution as described by Soleimany et al. . We selected these three methods as they represent the naive approach, then a more sophisticated approach, and lastly a state of the art approach to active learning for regression tasks.