Metabolomics, the study of small molecules, has recently emerged as the latest addition to the suite of 'omics' disciplines. A pivotal platform in untargeted metabolomics is Liquid Chromatography-Mass Spectrometry (LC-MS), renowned for its superior sensitivity and resolution. Additionally, Gas Chromatography-Mass Spectrometry (GC-MS) is widely used along with LC-MS depending on the type of samples and metabolites of interest. A critical phase in untargeted metabolomics is the identification of compounds. This process, whether structure-based or library-based, fundamentally involves calculating similarity scores between the experimental mass spectra and the corresponding library or theoretical mass spectra. A plethora of similarity scores have been developed to facilitate compound identification, broadly categorized into two types: binary and continuous similarity scores. Binary similarity scores simplify continuous intensity levels into two categories: zero and non-zero, according to the status of intensities (i.e., zero vs. non-zero intensities), subsequently deriving similarity scores from these binary categorizations. In contrast, continuous similarity scores leverage the complete spectrum of continuous intensity data, which generates more reliable and accurate identification results. This study concentrates on continuous similarity scores. Among the existing continuous similarity scores, the Cosine Correlation, also known as the dot product, is frequently utilized in compound identification due to its straightforward implementation, cost-effective computation, and consistently robust and reliable results.