Remarkably, the image-based clustering demonstrated significantly higher ARI scores than the numerical data for both MACCS keys (0.663 vs. 0.336) and RDKit (0.679 vs. 0.251) fingerprints. This suggests that the image analysis captures chemical relationships more effectively, aligning more closely with the fingerprint-based classification and is, therefore, more valuable. However, the Morgan fingerprint showed less agreement with both image and numerical data (0.204 vs. 0.167), possibly indicating that this particular fingerprint may not be well-suited for classifying the compounds in our dataset. Moreover, a closer examination of the hierarchical clustering results revealed further distinctions. For instance, all brucine molecules were correctly grouped within a single small cluster (A-2-2-2-1) in the imagebased analysis. By contrast, the numerical data analysis (Figure , the small cluster on the bottom) placed these molecules in a cluster mixed with other compounds. These observations suggest that image data provide superior classification performance than numerical data of the same resolution. Although increasing the resolution of the numerical data might reduce this performance gap, this will be explored in a future study. Principal Component Analysis. To further explore the relationships within the data, we performed PCA to reduce the 208,620 dimensions to 2 dimensions, followed by kmeans clustering (Figure ). The resulting scatter plot shows a rough division between aromatic and aliphatic compounds on either side of the dotted line. Analysis of the superposition images for each k-means cluster revealed patterns consistent with those of the hierarchical clustering results. Cluster 0, 3, and 5 highlighted the peaks associated with C-H bonds (approximately 3000, 1500, and 700 cm -1 ), similar to hierarchical clusters A and B. Cluster 1 also highlights the C-H peaks but showed a clearer pattern.