The multi-task Bayesian optimization algorithm used in this study is open-source and is released as a package within the Summit framework previously reported by our group. This step towards utilizing machine learning and previous reaction data for future optimization campaigns will ultimately result in faster and more efficient optimizations, thereby serving as a broadly applicable enabling tool with relevance to medicinal chemistry and FBDD settings, where industry-standard process optimization techniques are impractical or even impossible to implement.