To show that RF performance can be predicted based on the RF kernel with CKA, we trained RFs with varying circular fingerprint radii r ∈ {1, 2, 3, 4} or physicochemical properties as features. We used 206 physicochemical descriptors from RDKit (all except 'Ipc' and 'BertzCT', which produced double overflows for large molecules).