Methods for comparing two embeddings derived from a data set should correlate embedding similarity with performance similarity. Most of the existing methods either aim to find a correlation between the embeddings or compare the manifolds on which the data points lie. The latter type of method is based on the manifold hypothesis that high-dimensional real-world data points lie on a comparatively low-dimensional manifold. Historically, canonical correlation analysis (CCA) was used to infer information from the cross-covariance between two sets of random variables. However, CCA is unstable to perturbations if the embedding matrices are not well conditioned. This limitation has been addressed with singular-vectors canonical-correlation analysis (SVCCA), which performs CCA on representations of truncated singular value decompositions of the embeddings. Another approach to limit the sensitivity of CCA to changes in the input is projection-weighted CCA (PWCCA). PWCCA replaces the mean in CCA with a weighted mean. It is also possible to compare embeddings by finding the minimal rotation from one embedding projected on the unit ball to the other. This problem is known as the orthogonal Procrustes problem. There are also topological methods for comparing embeddings available, such as the Intrinsic Multi-scale Distance (IMD). IMD estimates a lower bound for the spectral Gromov-Wasserstein distances between two data manifolds by analyzing the heat kernel traces of Laplacians from k-nearest neighbor graphs constructed from feature matrices. Representation Topology Divergence (RTD) compares two embeddings by considering each represen-tation as a graph, with nodes being samples and the edges containing the distance between two samples. RTD evaluates the disparity in the topology of these graphs to quantify the differences between embeddings. Another method for comparing embeddings is centered kernel alignment (CKA), which was designed to fulfill the intrinsic properties of NNs (e.g. invariance to orthogonal transformation). It is based on the Hilbert-Schmidt Independence Criterion (HSIC), which tests whether two sets of variables are independent. Calculating the CKA with the linear kernel is magnitudes faster than RTD and IMD. CKA has been used, e.g., for natural language processing 20 and vision tasks, where it excels at correlating layers of two networks with the same architecture but different training seeds. CKA was also used to determine the robustness of models to shifts in graph size by comparing node embeddings of original and coarsened graphs. . The work's main contribution is the adaptation of the kernel of CKA for RFs to match their non-invariance to data rotation and enhance insight acquisition (Figure ). We illustrate the utility of CKA in three case studies relevant to cheminformatics: (1) an investigation of why optimizing the radius of circular fingerprints beyond two bonds results in only minor changes in the performance of models, (2) an analysis of the dependence between physicochemical properties and the molecular representations induced by GNNs that use addition as readout operation, and (3) a comparison of different graph readout operations in GNNs.