We investigated different sized initialization datasets for all 18 binary buffer systems, see Figure . When analyzing the results, we want to directly compare the total number of datapoints (i.e. pH measurements) required to obtain the target pH, hence the sum of the number of datapoints within the initialization dataset and the number of datapoints obtained during the experimental iterations. Overall, using only two initial datapoints resulted in the fastest method, requiring on average 5.8 ± 0.6 total pH measurements, followed by 6.3 ± 0.6 and 6.8 ± 0.5 pH measurements for three and four initial datapoints, respectively. When initializing a model with two datapoints, the subsequent two datapoints are chosen selectively as opposed to using four random datapoints for initialization. The results indicate that the selective choice of the active ML strategy seems to be beneficial over random datapoint allocation, irrespective of the fact that the preliminary model is solely trained on two datapoints.