It is important to note that features such as morphology, phases, or surface facets undoubtedly influence the electrocatalytic performance. However, such features often come as a result of compositional tuning and can turn out to be secondary information for material discovery campaigns. Such information becomes indispensable when focusing on revealing a detailed structure-property relation to, e.g., derive new descriptors. Implementing X-ray diffraction (XRD) or X-ray photoelectron spectroscopy (XPS) into SDLs to extract such supportive information adds more engineering complexity. Hence, recording the electrocatalytic performance as a function of the composition remains a popular route for automated HT workflows in electrocatalysis and represents an intuitive research question in material science. Figure depicts the obtained ηOER and ΔηOER for each composition. The data set can be found in Table in the Supporting Information. It is noticeable that the lack of Co or Mn manifests in a massive activity drop indicated by the increase in ηOER around experiment count 55. This result aligns with expectations, as Sb, Sn, and Ti oxides are known to be poor OER catalysts. An obvious trend in the stability results was not observed. Surprisingly, some ΔηOER are negative, indicating that the catalyst became more active after the AST. Overpotentials typically increase as a result of deactivation. A so-called activation step, as encountered for noble metals or MEAs, was not observed in the majority of the tested samples. A follow-up investigation on this irregular phenomenon will be present towards the end of this study. For now, it seems that most samples exhibit a ΔηOER around 0 -20 mV. The data shown in Figure was used to simulate a MOBO process without running additional experiments to evaluate if the algorithm can quickly identify the most active and stable compositions within the data set. qNoisyExpectedHypervolumeImprovement (qNEHVI) was chosen as the acquisition function to identify the Pareto front as is can weigh trade-offs among multiple objectives. Additionally, it does not require a prior selection of a known trade-off between the objectives. A FixedNoiseMultiTaskGP surrogate model was used as ML model, which allows for the inclusion of experimental noise determined during the HT screening into the model. The algorithm was initialized with five random entries. Based on the results in Figure , a ηOER of 550 mV and a ΔηOER of 10 mV were determined as suitable reference points for the optimization, presenting values that are both desirable and realistically achievable. After initialization, the MOBO algorithm picked one new candidate from the remaining data set per iteration, rearranging the data as shown in Figure . By constructing a Pareto plot by mapping the activity over stability, it becomes clear that the algorithm rapidly selected points within the optimal quarter (left bottom corner) during the first 20 iterations. Once no better compositions are left within the data set, the points scatter to less active and stable regions. To evaluate the speed of optimization, the FixedNoiseMultiTaskGP model was benchmarked against a random sampler that does not learn from previous iterations. For this comparison, the normalized hypervolume is plotted over each sampling iteration. The hypervolume is the area that spans between the Pareto optimal points and the reference point. When dealing with random sampling, multiple repetitions are required to probe the statistical significance. If the random sampler happens to sample the best compositions at the beginning, and this repitition is compared, it would insinuate MOBO to be inferior. Figure shows the comparison of the FixedNoiseMultitaskGP surrogate model against a random sampler after 500 repetitions. The adaptive sampling strategy indeed outperforms an uninformed sampler and finds the optimum already after 20 iterations. The FixedNoiseMultiTaskGP model was also compared to a MultiTaskGP model that does not consider noise. The performance is just marginally worse (see Figure ). However, as heteroscedasticity (different variance in each data point) is an important information during ML-guided HT screening, the FixedNoiseMultiTaskGP was chosen as the surrogate model for the subsequent MOBO-driven experimental optimization of the Co-Mn-Sb-Sn-Ti oxide space.