Often during a lead optimization campaign, it is more effective to use a Random Forest for molecular property prediction because of small dataset sizes. This study suggests a model, DeepDelta, that directly compares two molecules and learns to predict property differences between them. They find that this approach does better than a message passing neural network or a Random Forest when evaluated with Pearson's r and MAE metrics.