Table : Each experimental case study explored in this work, including the starting material used, the product formed and the API structure that the product is linked to. These reactions, and references to their known API structure, are highlighted in Schemes 3 -6. For all experimental work conducted during this study, a self-optimizing flow reactor platform was utilized with a control interface previously disclosed by our group. This platform employs an autonomous optimization workflow, where all experiments are conducted and analyzed without human intervention. All initial training experiments are planned using LHS, then the results from these automated experiments (the yield of the product) are exported using on-line HPLC sampling. Based on these reaction data, and previously conducted auxiliary tasks, the MTBO algorithm then determines the most beneficial reaction conditions to execute in order to maximize product yield. The actual product yield obtained from this reaction is then communicated back to the algorithm, where the experimental feedback loop is closed, as the algorithm suggests conditions for the next optimization iteration (as shown in in Figure ). Furthermore, only minimal amounts of reaction material are consumed in each experiment by using reaction slugs; this is an important miniaturization consideration relevant to medicinal chemistry settings, but could potentially be miniaturized further. The minimum slug length is determined on the basis of dispersion in laminar flow such that sampling from a slug is consistent between slugs in repeated tests -the volume of the slugs used in these studies is 4 mL. The aim of this experimental methodology is to accelerate the optimization timeline by requiring fewer experiments and less reaction material consumption. More information on the reactor setup can be found in the Methods section. For each case study, we optimized the continuous parameters: residence time (5 -60 minutes), reaction temperature (50 -150 °C) and catalyst concentration (1 -10 mol%), and the categorical parameters: solvent (toluene, DMA, acetonitrile, DMSO, NMP) and ligand (JohnPhos, SPhos, XPhos, DPEPhos), for the maximum product yield output. While it is possible to represent these categorical variables in numerous ways, the simplest representation (one-hot encoding) proved sufficient to learn from. The first case study, as shown in Scheme 3, utilizes only single-task Bayesian optimization (STBO) as there is no previous data to leverage model understanding for MTBO. The starting material, 17, reacts to form the molecular fragment (with potential growth vectors for further functionalization), 18. The optimization was initialized using 16 ) training experiments before the algorithm began to suggest experimental conditions.