As shown in Figure , Bayesian optimization (BO) relies on three key components: a probabilistic model, an acquisition function and an optimization algorithm. The probabilistic model is trained using experimental data and acts as a surrogate or "simulation" of the real chemical reaction. Given this probabilistic model, the acquisition function estimates the values of different potential experimental reaction conditions. The optimization algorithm is then used to find the set of experimental conditions that maximizes the acquisition function, and these experimental conditions are hence suggested as the next real experiment to run. The combination of the probabilistic model and the acquisition function enables exploitation of known high performance areas and exploration of new chemical space. By iteratively executing the suggested experimental conditions, retraining the model and optimizing the acquisition function, the BO protocol progressively identifies the best reaction conditions for the output of interest. As shown in Figure , MTBO changes the probabilistic model in BO. Typically, a Gaussian process (GP) is used as the probabilistic model in BO due to the general applicability and efficiency of GPs in the small data regime. MTBO replaces a GP with a multi-task GP that can learn the correlations between different tasks to enable better predictions. In our case, the tasks are chemical transformations from the same reaction class with varying substrates. A formal definition of GPs and multi-task GPs is in the Methods section.