As in the previous analysis, two types of splits were tested for each prediction task: a random split and a publication split. For all tasks and splits, the RF models perform best, with fingerprint and OHE-based representations performing similarly. Recall that our OHE of donor molecules separately encodes each substituent and still defines its stereochemical configuration, rather than encoding the overall donor molecule as one discrete choice. The results are displayed in Table . For the major anomer prediction and minor product formation classification tasks, the models perform adequately for both the random split and the publication split with AUCs ranging from 0.89 to 0.97. For the anomeric ratio regression task, the models only yield a satisfactory performance for the random split, with a large drop in performance for the publication split, reflected by a drop from 0.57 to 0.11 in R 2 -score for the bestperforming models. Understandably, it is easier to build models that can accurately predict the major anomer product than the actual ratio of products for the publication split. Recall previous work by Chang et al. (RMSE: 15.1% & R 2 : 0.82) and Moon et al. (RMSE: 2.0-19.3% & R 2 : 0.98-0.81) where evaluations more closely resemble the random split, in the sense that the test set comprises of glycosylations with the same glycosylation methodologies as the glycosylations in the training set with only one reaction parameter not being present in the training data for each glycosylation. Contrary, the test set for the publication split includes new glycosylation methodologies with multiple reaction parameters not being present in the training set. Our quantitative performance results appear strong when taking into account that our data comes from multiple different publications, covers a broad chemical space, but is highly sparse if considering all possible combination of substrates, activators, solvents and temperature.