The reaction condition prediction model used in this work predicts five categorical variables: two solvents and three agents. These five molecules form a set (order invariant), though the loss function in the model used to predict the molecules considers them sequentially (with order) since this was found to work better in practice . The metric used to evaluate the accuracy of the model should be order invariant, since the problem is order invariant, and for this reason the accuracy metrics used are top-1 (see appendix C) and top-3 (see Table ) exact match combination accuracy for each type of component (i.e., solvent, agent). Beam search was used to identify the top-3 highest probability sets of reaction conditions. The top-3 accuracy was compared to the baseline predictive accuracy of simply predicting on the test set the most common molecules found in the train set.