After the initial training experiments, the feedback loop (as described in the Methods section) was implemented and 7 further experiments were conducted, finding the optimal reaction parameters of: NMP, XPhos, 53 minutes residence time, 89 °C reactor temperature with 9 mol% catalyst, yielding the product, 18, in 74.6 % yield. These results are interesting, because with many reported optimization campaigns the optimal conditions are often the most forcing (highest temperature, highest residence time, highest catalyst concentration) However, in this case, the algorithm determines that a moderate reactor temperature is important for a higher yield. This is because the starting material reacts to form other products, leading to a decrease in the desired product yield under more forcing conditions. Furthermore, the optimized conditions reported in the original publication describing these types of reactions feature toluene and JohnPhos, which are different from our optimized parameters for this reaction. However, these reported conditions require reaction times of 2.5 -6 hours which are difficult to replicate in flow, which could be the reason why the same categorical parameters were not determined to be optimal in our 5 -60 minutes residence time optimization space. A plot of the experimental data, both training and optimization experiments, and the yields achieved are shown in Figure . These 23 experiments required to achieve optimal conditions are also significantly fewer in number than what would be required for current industrialstandard optimization procedures, such as design of experiments (DoE), which would require >750 experiments of efficient design space exploring data points. All reaction data for each case study is reported in full in the ESI, as well as efficiency comparisons with industrial-standard optimization procedures. Utilizing the experimental dataset from this optimization campaign, a different substrate was then explored in the second case study. The starting material, 19, reacts to form a key intermediate for a serine palmitoyl transferase (SPT) inhibitor, 20, as shown in Scheme 4. As this is a similar transformation, the use of MTBO should hasten optimization and produce optimal reaction conditions much more quickly. The optimization is initiated, and the first suggested experiment deviates only slightly from the previously obtained best parameters, whilst still utilizing NMP and XPhos as the categorical variables but produces a poor yield of the product (14.8 %). As this yield is much lower than what the underlying multi-task model had predicted, the corresponding weightings to select this area of parameter space for this case study are greatly reduced and thereby the likelihood of exploring this area again during this campaign is reduced. The model then balances the exploration of new parameter space with the exploitation of known favorable conditions, particularly from the previous case study, to iterate through further experiments. The optimal reaction conditions were found in 11 experiments: acetonitrile, JohnPhos, 28 minutes residence time, 127 °C reactor temperature with 5 mol% catalyst, yielding the product, 20, in 84.9 % yield. It is important to note that this area of parameter space is far from the identified optimum in the previous case study, showing the adaptability of MTBO to similar optimization tasks without simply exploiting near the previously obtained optimal conditions. To identify these process parameters, this entire workflow consumed only 980 mg of the starting material, 19, and has a much greater throughput (requiring less catalyst loading, cheaper materials and non-complex solvent mixtures) than other reports of this chemistry that yield only 76 % of the desired product. This experimental data is displayed at the end of this section in Figure (red dotted line).