In this set of experiments, we compare the performances of various configurations of chemical space visualization on dealing with the APC problem. While traditional methods like Structure-Activity Relationships (SAR) and Structure-Activity Landscape (SAL) indexes provide quantification and detection of APCs of molecular datasets, there is no available method for quantifying APCs for chemical space visualizations. To measure the activity/property difference of similar (i.e. close) molecules in the visualized chemical space, we use the herein introduced metric of the DPR scores. Unlike SAR and SAL indexes, the DPR score defines the similarity based on distances of molecules on the reduced chemical space as opposed to basing it on their common chemical substructures. To achieve this, the DPR algorithm first calculates the Euclidean distances between all pairs of molecules and sorts them in ascending order (i.e. closest first). Next, for a selected top percentage, an average of the property difference is calculated. Table shows the top 1, 2, and 5% DPR scores of the chemical space visualizations that are obtained by using various experimental configurations. On all datasets, among the three dimensionality reduction methods, t-SNE method produced the best DPR scores, while UMAP performed better than PCA. In addition, for the majority of experiments, the tailored similarity showed a better performance than the structural similarity.