Curated and tailored high-quality reaction data sets are the cornerstone for conducting successful reaction forward prediction. The data used for machine learning in this study consisted of one Minisci-type alkylation data set with 13,490 transformations covering a broad range of scaffolds relevant to medicinal chemistry. Data points were generated in a resource-and time-efficient HTE setup, requiring minimal starting material. This experimental approach enabled us to explore a relevant portion (12%) of the vast reaction space, encompassing approximately 115,000 potential combinations. The sampling proved sufficient for robust machine learning model training, demonstrating the efficiency of our approach. To make the data available for reaction prediction, it was exported in SURF . SURF files provide the reaction information in a comprehensive machine-readable format containing all parameters of the transformation, as well as structural and quantitative information of all components and products. Similar to related recent work, it became ev-ident that such machine-readable data sets including negative reaction outcomes are the key enablers for successful reaction prediction.