Renewed interest in the biological role of lipids coupled with technological advances in mass spectrometry has led to an increase in number of published lipidomes. Studies investigating the impact of lipidome changes accompanying developmental processes and disease progression are expected to increase in coming years. However, methods to visualize and quantify “qualitative” changes in lipidome are limited. Aim of this study is to develop informatics resources that account for chemical similarity between lipid molecules as basis for a qualitative comparison of lipidomes. This study investigates 1) How large ensembles of lipid molecules and their chemical structure can be computationally represented 2) Which algorithms are suitable for unsupervised determination of chemical similarity between lipid molecules 3) How to visualize lipidomes consisting of hundreds of molecules? Lipidomes can be embodied as databases of SMILES strings, which are handled as individual sequences. We performed systematic analysis of a number of string comparison algorithms (Levenshtein distance, Smith-Waterman Local Alignment, Multiple Sequence Alignment etc) for efficient and accurate measurement of chemical similarities. As results, we show that alignment based algorithms resolve minute positional changes in isomeric lipid molecules and have the potential to differentiate all molecules of a Lipidome. As a test dataset, we generated 1185 molecules that are comprised of 21 Fatty Acids, 12 Sphingoid bases, 288 Ceramides, 288 Ceramide Phosphoethanolamines, 288 GlucosylCeramides and 288 Ceramide Phosphi-Inositols. Our approach based upon Non-Canonical SMILES representation and Multiple Sequence Alignment as chemical similarity measure, outperformed all other tested methods and an analysis on a 3.07 GHz processor would take 2.73 minutes. Finally, we tested the practicality of our approach by performing similar analysis on the entire LipidMaps database using dimensional scaling of inter-molecular similarities. Our unsupervised informatics approach structured and separated all lipid classes according to LipidMaps classification. We envision that this kind of informatics approach will help to understand the influence of lipid metabolism in cell differentiation, degenerative processes and usage of disease models.