Employing CKA for molecular embeddings, we show that the radius of circular fingerprints has no substantial effect on the performance of RFs because the rules learned by the forests are similar. Additionally, we demonstrate that CKA can be used for differentiating types of readout operations in GNNs. In our experiments, trained and untrained sum pooling GNN embeddings were aligned based on CKA. Further, we show that physicochemical properties can be more closely aligned with sum pooling than max pooling and provide a plausible explanation for this behavior.