The studies performed in this work, both in silico and in real-world chemical applications, represent the first use of datasets from similar reactions to expediate current optimization campaigns with multi-task Bayesian optimization. This methodology drastically shortens optimization timelines for pharmaceutically relevant transformations, whereas other traditional process optimization techniques (i.e. design of experiments, kinetics studies) would require a significantly higher investment in starting materials, time and cost. This would likely make their optimization infeasible in medicinal fragment-to-lead/FBDD workflows and early-stage process development, unless using intuition-based optimization techniques (such as OFAT) that are unlikely to obtain optimal results. By introducing more miniaturization technology, including smaller reactors/slugs and plate-based screening, there is the added opportunity to reduce material consumption even further using these automated platforms.