As shown in Figure , we developed a benchmarking workflow that converts unstructured data into benchmarks that can used for comparing various strategies. We leveraged the Open Reaction Database (ORD) format as a common representation of reactions. We wrote converters from spreadsheet formats to ORD. Once the data was transformed into ORD, the data was loaded into local storage on disk for featurization. Subsequently, the featurization step turned the ORD schema into a set of features that can be used for training a benchmark or a GP for Bayesian Optimization. We used one-hot encodings to represent the categorical variables.