In this work, we analyzed linear and RF kernel for CKA by the example of MLPs and RFs in the context of molecular property prediction. More specifically, we examined RFs and MLPs with respect to their rotational invariance and conclude that kernels for CKA should be aligned with the properties of the model. We show that using CKA with RF kernel has several advantages over assessing similarity of RFs based on prediction similarity.