The 20 lowest-λ structures from all three screenings are shown in Fig 6 . Interestingly, 15 compounds in this subset were identified by the GFN1-xTB screening, while the ∆K s and ∆K sp models identified 9 and 11, falling slightly behind. In other words, the GFN1-xTB model actually has an edge over the ML model when considering the extreme low end of the distribution, although it is in general less effective in identifying low-λ structures. It is also notable that, although some overlap between the methods is observed (i.e. from the 1500 molecules selected by the three screenings only 1131 are unique candidates), many structures are exclusively identified by one method, in particular by GFN1-xTB. This is illustrated by the Kernel Principal Component Analysis map shown in Fig , which places similar molecular structures close to each other. Clearly, the semiempirical GFN1-xTB model overall exhibits the highest diversity, while the candidates selected by the data-driven models appear somewhat more concentrated. This reflects the fact that GPR models use metrics of molecular similarity in their predictions.