The second group of experiments was done utilizing a different way to select molecules to add to the training data. In order to do this, we trained a Gaussian Process model with the Radial Basis Function kernel on the data, utilizing 196 RDkit descriptors as input instead of molecular fingerprints. We utilized maximal Information Gain as the active learning criteria for our Gaussian Process model. Each of these differences was performed in order to have this new ordering of molecules be as different as possible from the ones proposed by our model in Figure . We hypothesize that the informativeness of a molecule in the withheld set is a property inherent to said molecule, and since our initial active learning approaches failed to identify informative molecules, we sought to evaluate a different schema that was unrelated from our model training process. Thus, we evaluated each of our fingerprint models on the proposed ordering from the Gaussian Process and a random ordering of the first 50 molecules to add to the training set for 10 different random splits of this new training data (Figure ).