It also gets clear from the top part of Figure that the optimiser explored more of the chemical space for the MD tenant than for the ASAB tenant. Possibly, this is due to the significantly larger region of the chemical space being accessible to the MD tenant than to the ASAB tenant. However, it also gets obvious that the optimiser requests a lot of formulations close to the edges of the chemical space of the MD tenant. This behaviour likely occurs due to the model trained by the optimiser having a high uncertainty near the edges of the accessible chemical space. Regions with high predicted values and high uncertainty bear the potential for formulations with high ionic conductivity and are therefore sampled by the optimiser acquisition function. A larger number of randomly selected starting formulations (1 in the single-task phase vs. 10 in the multi-task phase) seems to have resulted in a better initial model and consequently improved exploration of the chemical space in the multi-task phase of the campaign as can be seen in the bottom part of Figure .