We observed that the Cosine Correlation, especially with the weight factor transformation, outperformed both the Shannon and Tsallis Entropy Correlations regardless of whether the mass spectra were ESI or EI. Additionally, the novel Tsallis Entropy Correlation showed higher accuracy than the Shannon Entropy Correlation, although the difference was not statistically significant. This higher accuracy is attributed to the Tsallis Entropy Correlation having an additional tuning parameter, the entropy dimension, which makes it more versatile than the Shannon Entropy Correlation. However, this additional tuning parameter also makes the Tsallis Entropy Correlation more computationally expensive. The superior performance of the Cosine Correlation was evident in EI-based identification, although it was not statistically significant in ESI-based identification. In terms of computational expense, the Cosine Correlation is the most efficient among the three measures. These findings confirm that the Cosine Correlation is both computationally efficient and highly accurate, making it a leading similarity measure for MS-based compound identification. The numbers within square brackets represent 95% CI; the numbers within parentheses represent the set of parameters, low-entropy threshold (LET), mass-to-charge ratio weight factor, intensity weight factor, and entropy dimension (ED), respectively. It is noted that ED applies only to Tsallis Entropy Correlation.