More recently, deep-learning methods have become attractive for predictive modelling due to their convenient implementation, and graph-based neural networks in particular allow the structure of a molecule to be imported directly into a machine learning model. ChemProp, a message passing neural network (MPNN) model working on molecular graphs, managed to outperform fixed representations and prior deep learning models. In this paper, we explore the utility of MILs and MPNNs to predict the biodegradability of unseen compounds. The metric used here, the biochemical oxygen demand (BOD) , serves as a good predictor for RB compounds that builds on previous experimental metrics like degradation time and half-life 1 . We conduct benchmarking for various prediction models with this set of data, emphasizing features like 3D chemical descriptors and congeneric compounds.