As everything we have tried thus far has failed, we hypothesized that utilizing the Morgan fingerprints as input to our model is not expressive enough for the active learning methods to perform well. Thus we sought to provide a more feature rich input to our model. We selected the pre-trained weights of the MAT 21 model as our input representation as the authors were successful in using them on other molecular property prediction tasks, and the pre-training procedure they employed of having the model predict the atom properties of masked nodes would imbue their network with some knowledge of chemistry. Thus, we hypothesize that by taking the output of the generator portion of MAT as the input to our model, we could provide a more feature rich input which could boost model performance.