The procedure for generating poses through multitemplate pairing and 2D3D scoring has previously been described and the code has been made publicly available . A total of 143 Roche internal co-crystal structures served as templates for docking. For each ligand, the template structure was selected on the basis of the maximum common substructure (MCS) between the ligand and the template ligand. The pose was generated with the GOLD docking program by constraining the ligand to the MCS part of the reference ligand . The template docking approach was used as a data augmentation method to generate virtual protein-ligand complex structures for 3245 compounds with measured activities that serve as training data for the 2D3D scoring function, as described . The 2D scoring model within the 2D3D scoring function is an attentive fingerprint (AttentiveFP) model trained on Roche in-house data. Using a four-fold cross-validation on random splits of a data set containing 7487 compounds with annotated biological activity to MAGL, this 2D model achieved a coefficient of determination (R 2 ) of 0.72 and a root mean square error (RMSE) of 0.77. The 3D scoring model relies on a frequency interaction ratio and is taken without further adaptation from the original publication, representing a statistical model trained on data. The final "2D3D scoring" function is obtained by combining these 2D and 3D models into a single scoring metric. Subsequently, a virtual library of prospective ligands was subjected to multiple template docking, and the poses generated were ranked using the MAGL 2D3D scoring function.