Numerous molecular descriptors can be extracted from molecules with as little as their structural formulae. These include physicochemical properties, connectivity-based descriptors, and tailored molecular fingerprints that compare molecules based on distinct calculated features. With biodegradation of compounds, knowledge and experimental methods have improved vastly and theoretical research enables the transfer basic chemical rules to experimentally or computationally measured properties of these compounds. With databases like that obtained from the Japanese Ministry of International Trade and Industry (J-MITI) test , comprehensive data is now available to characterise biodegradation trends across a diverse range of compounds, increasing their applicability to other compounds in general. However, approaches make use of combinatorial probability models derived from various feature reduction and ML types, making a consolidated approach more attractive. Prediction of biodegradability has been carried out with models with the aim of incorporating a holistic understanding of molecules. Although the earliest methods were focused on structural features and abstract molecular representations for various machine learning frameworks , subsequent work has expanded the range of descriptor tools, including constitutional and topological indices. In comparison, the role of 3D structures has received less attention for such predictions, being explicitly excluded from earlier work due to cited problems in their repeatability and slower predictions . On the other hand, individual 3D conformers can be further analysed to produce spatially independent geometric descriptors, but the use of a multi-instance learning (MIL) approach allows for a more all-encompassing and accurate interpretation of 3D-based descriptor data. In particular, the applicability of computationally expensive density functional theory methods remains to be seen. We decided to compare conformers generated with the Conformer-Rotamer Ensemble Sampling Tool (CREST) software to low-level conformers generated by RDKit.