Structure elucidation often requires data from several sources. For example, combining a 1D NMR technique such as a proton ( 1 H) or carbon ( 13 C) spectrum and 2D methods like Heteronuclear Single Quantum Coherence (HSQC) or/and Heteronuclear Multiple Bond Correlation (HMBC) can be beneficial. However, practical limitations such as overlapping peaks, impurities, or product mixtures can introduce sources of error rendering the elucidation of the correct structure a highly challenging task. This is reflected in a growing number of natural and synthetic products with incorrectly assigned structures. HSQC is a 2D NMR technique based on linking proton ( 1 H) and carbon ( 13 C) chemical shifts, offering a valuable balance between information content and data collection time. These spectra are contour plots that reveal high-intensity spots or "peaks", denoting direct C-H correlations within the target molecule, serving as a unique molecular fingerprint. Although information rich, the sparse, pseudodiscrete 2D nature of these fingerprints make straightforward comparisons between HSQC spectra challenging due to issues with point matching. The ability to evaluate spectral similarity, that is how well two spectra match, is a basic requirement for comparison of newly-obtained spectra with existing data. While for 1D techniques, lots of work has been done in this area, literature on effective comparisons of 2D spectra is largely absent. In this study, we address this deficiency by focusing on HSQC data, aiming to investigate, develop, and evaluate diverse methodologies for 2D spectral matching, with the ultimate objective of enhancing accuracy and efficiency in tasks such as compound identification and database searches.