Our model can be an effective baseline for future studies due to its high accuracy and relative simplicity. In the future, it may be interesting to explore the relative merits of more complex deep learning architectures versus similarity-based methods. For example, approaches employed here may have similar performance to more complex neural networks if out-of-sample data resembles existing data, such as during medicinal chemistry efforts or when only small numbers of building blocks and compounds have been explored. On the other hand, when the amount of available data becomes large, it seems likely that deep learning models perform better.