With the increasing density of chemical reaction data, both in the literature and in private data storage, there is a wealth of information that can be leveraged for building task-specific models to further increase the efficiency of future reaction optimizations. When using these multi-task learning approaches, it is possible to generate sets of models for specific reaction classes (e.g. Buchwald-Hartwig, Suzuki etc.) and subsets of those models (electron-rich, sterically hindered etc.) to rapidly optimize any transformation likely to be encountered. This is a particularly powerful technique in cases where starting materials are sparse and the reaction is poorly understood, yet suitable quantities of product is required for further molecular design, functionalization and biological testing. Similarly, this importance is echoed in early process development when scale-up of a novel synthetic intermediate is required from the milligram scale to multi-gram or kilogram scale.