Initially, the correlations among the three similarity scores were explored using heatmaps, without considering compound identification. Then, we investigated the relationships between identification accuracy, preprocessing, and the order of preprocessing, identifying the optimal order and parameters for each similarity measure. Subsequently, the ROC curves were assessed in terms of AUC. Lastly, we conducted consensus analyses of compound identification among the three similarity scores using Venn diagrams and UpSet plots. Using 1,000 bootstrapping iterations with replacements, 95% confidence intervals (CIs) for accuracy were computed. For AUC, the 95% CIs were computed using DeLong's method. The parameters that were used for preprocessing were as follows. Regardless of the choice between ESI and EI, we considered two types of transformations: intensity weight factor transformation (WFT) and low-entropy threshold transformation (LET). For WFT, we em- factors were selected based on findings from previous studies. For LET, we considered two thresholds, 0 and 3, where a threshold of 0 implies that no low-entropy transformation was performed, and a threshold of 3 was selected based on Li et al. (2021). Specifically for the Tsallis Entropy Correlation, the entropy dimension (ED) parameter, q, was further varied across 17 values: 0.5, 0.7, 0.75, 0.8, 0.9, 0.95, 1.05, 1.