The choice of the size of the initial dataset represents a trade-off between showing the model enough information so that initial predictions are useful and keeping the dataset sufficiently small to limit the amount of experimental time and resources used. We chose four different sizes of initialization with five, 10, 15 and 20 random datapoints. As shown in Figure , the results indicate that larger sized initial datasets allow the model to predict conditions that give >99% yield in fewer iterations. Using 20 random datapoints for initializations allowed finding the global optimum on average within 40 iterations while using only five initial datapoints required up to 100 iterations. In total 186 datapoints are available. Whilst the choice of the adequate size of the initialization dataset might vary on the parameter space of the dataset as well as the size and complexity of the prediction space, we assume that a minimum of 15-20 datapoints should be chosen -here this choice allowed for identification of optimal conditions in approximately 45 iterations. Overall, we suspect that the smaller sized initialization datasets are detrimental as biases may get introduced from the beginning, particularly since a greedy search (no exploration) was used. This leads to negative impacts when the active ML is conducting mostly extrapolative predictions (see the low performance of extrapolation described previously). In the case of the small (e.g., five datapoints) initialization datasets, this effect was severe for the learning curves. However, being restricted to a local initialization dataset (data from three ligands) led to very steep initial learning curves and a performance similar to the initialization with a more diverse dataset, thus demonstrating the power of successful navigation through the chemical space by active ML driven closed-loop optimization.