For all models, including the RF trained on the vectorized images, the Matthews correlation coefficient (MCC) was above 0.8 (Table ) . In contrast, the performance of the RFs For NNs based on MLPs, using a rotational invariant kernel is an adequate choice because the convolutional encoder learns a representation that can be rotated in any direction and magnitude. Hence, CKA with the inner product as the kernel (CKA linear ) is a common similarity measure for MLPs. We investigated the capacity of CKA rf to distill information about RF models at the example of 8 tasks on cytochrome P450 substrate and inhibition prediction (tdc ). More specifically, we applied CKA rf to RF models derived from circular fingerprints of radius one to four (FP r with r ∈ {1, 2, 3, 4}) and RF models derived from physicochemical properties (PC). A total of 200 models resulted from the combination of five RFs trained for each of the five embedding strategies and eight data sets. Across all data sets, we recorded high CKA rf values between the embedding strategies of circular fingerprints of various lengths (Figure ). In contrast, the CKA rf between models with physicochemical properties and circular fingerprints was considerably lower. Even though the data sets vary in difficulty (our models obtained a mean MCC of 0.575 ± 0.069 for the tdc data sets derived from the work of Veith et al. and a mean MCC of only 0.243 ± 0.063 for the tdc data sets derived from the work of Carbon-Mangels et al.; details provided in the supporting information, the results are consistent. This is because CKA can detect similarities among models with distinct performance, as discussed in subsection 3.2. FP r denotes the models using circular fingerprints with radius r as features; PC denotes the models using physicochemical properties. Notably, the circular fingerprints of radius 1 exhibited lower embedding similarities than any other combination of circular fingerprints.