Recently, Taylor et al. introduced the idea of multi-task learning Bayesian optimization (MTBO) for chemical reaction optimization. Analogous to transfer learning in ML models, the idea behind multi-task learning is to pre-train the surrogate GP models with data that has been previously collected from similar reactions to eliminate the need of an initial sampling step and reduce the overall number of experiments required to obtain optimal reaction conditions. In MTBO, the standard GPR surrogate models are replaced with multitask GPs that use kernels able to create correlations between multiple GPs. The GP that models the experimental conditions that are being optimized is called the main task while any other GP trained on previous data are called auxiliary tasks (Figure ). The authors benchmarked MTBO in-silico for a single objective optimization for Suzuki-Miyaura reaction and discovered that pretraining the multi-task GPs using a single data set as an auxiliary in fewer iterations to achieve the optimal conditions in comparison to standard BO in most cases. Moreover, the authors observed that when four auxiliary tasks were used instead of one, the number of iterations to obtain optimal reaction conditions was reduced from 15 to fewer than 5 experiments (Figure ). Finally, the authors tested the performance of MTBO by performing the optimization in an automated flow reactor of a series of palladium catalysed C-H activation reactions of chloroacetanilides to produce their corresponding oxindoles (Figure ). For all reactions, three continuous and one categorical variable were optimized to maximize reaction yield. The authors first performed a standard single-objective BO of reaction (i) in Figure . The optimization was with a set of 16 distinct reaction conditions sampled by LHS, reaching optimal reaction conditions in 7 further BO iterations. Subsequently, a reaction (ii, Figure ) yielding a similar oxindole product was optimized using MTBO where the data gathered from the previous optimization was used to train the auxiliary GP, obtaining the optimal conditions in only 11 iterations in comparison to 18 required for the first reaction. A third reaction (iii, Figure ) yielding a similar oxindole product was optimized using the previous data from the first two optimization campaigns to train the auxiliary task GP. The authors found the optimal conditions by the algorithm in 5 iterations. Futher, the authors tested the ability of MTBO to learn from previous experiments by performing the optimization of two other C-H activation reactions where the structure of the C-H activation substrate (88) had a different structure in comparison to the first three optimizations. Thus, for the fourth campaign they tested the optimization of reaction that produced an a 6-membered nitrogen ring quinolinone instead of the 5-membered ring present in oxindoles. The MTBO was able to find optimal reaction conditions in 10 iterations, demonstrating the capability of the algorithm to handle the optimization of reactions that show small structural deviations Figure : (a) Comparison to standard GP (single-task) and multi-task GP, it can be observed that by training an auxiliary task using data collected from a similar reaction the uncertainties associated with the GP predictions are greatly reduced (adapted from ). (b) Comparison of reaction optimizations performed in silico for single-task and multi-task BO. It can be observed that multi-task BO requires a reduced number of iterations to find optimal parameters that maximize reaction yield. The performance is further increased by incorporating a larger number of auxiliary tasks (adapted from ). (c) Reactions used to test multi-task BO in experimental conditions. Reaction (i) was performed using standard single-task BO, each subsequent reaction incorporated the previously collected data to train auxiliary tasks. (d) Example of SeMOpt maximizing a sine function. The upper row shows the ground truth function with the sampled points and best suggested candidate by the BO algorithm. The bottom row shows the values from the acquisition function from the surrogate of the target objective, the NP process and their combination (adapted from ). from the auxiliary task. Finally, the limits of the MTBO were tested by using achloroacetanilide that presented an electron-rich aromatic ring (90), in this case the MTBO was unable to discover good performing reaction conditions.