Performance under these various settings is shown in Figure . A concatenation of the product features and difference in features between reactants and products (product and difference) seems to be the representation that gives the best performance, hence, this CGR-type was used for the models with additional features. Models with all stereochemical information removed still perform surprisingly well, likely due to the model assuming a gluco-like orientation of the C-2, which is the most prevailing orientation in the dataset (Section S2.1). Adding individual features gives minor improvements in general, and adding all the features gives the best performance. However, from the closely-clustered results it is difficult to pinpoint which features are most important. Noticeably, performance drops when adding the activators for the leaving group split (Figure ). This could be due to the high correlation between leaving group and activator(s) as glycosylation methods often rely on specific leaving group and activator interactions, which could indicate that the models learn trends for the LG/activator systems present in the training data in a manner that does not allow successful extrapolation to new leaving groups with the same activators.