Based on the previous findings on the size of the initial dataset and the different chemical representations, a direct comparison was conducted to assess the performance between complexity of parametrization and size of the initial dataset. Initialization datasets of 10, 15 and 20 datapoints were chosen along with OHE, Morgan 2 fingerprints and hybrid full feature representation (feature set 14, see Figure for more details). When using different sizes of initialization dataset the remaining data limits the number of possible active learning iterations. To allow for easy comparison of different sized initialization datasets, the data was normalized and, as such, at every location in the x-axis the different models have access to the same number of datapoints and so the effect of initialization dataset is represented.