Identification of small molecules is of major importance for many applications. Liquid Chromatography Tandem Mass spectrometry (LC- MSMS) is gaining increasing interest in the field of small molecule identification. LC-MSMS has a broad range of detection, is sensitive and does not need special sample pre-processing. As a major chal- lenge, spectra of the same compound can show great variability across acquisitions. High spectra variability limits the use of LC-MSMS for library search identifications. Dedicated identification tools such as MS Search from NIST show insufficient performances when it comes to cross-platform identification. In this thesis, we present the new library search scoring model X- Rank. X-Rank matches conserved properties of spectra and proposes a robust probability scoring model. Scoring parameters can be opti- mized from a training set. A re-training of X-Rank for a specific data set, was shown to essentially improve the results. The efficiency of X-Rank was compared to existing solutions, using two test-sets from different machine types. Overall X-Rank showed better results in terms of sensitivity and specificity. Especially in the case of cross-platform identification, X-Rank could better discriminate correct from wrong matches. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a test mix. Even though these results confirm an important improvement for cross-platform identification, filters before and after the X-Rank scor- ing are still useful. In this perspective, a new approach to confidently use the retention time information is presented. Furthermore, a spec- tra filtering approach is applied, which improves the identification in terms of quality and speed. Finally, using a specific training configuration, X-Rank was adapted for proteomics data. Combined with the peptide identification tool Phenyx, X-Rank helped matching additional peptides. X-Rank was implemented into the small molecule identification platform SmileMS. SmileMS is designed for a routine use in laborato- ries. It is a multi-user platform, which provides a simple identification workflow and intuitive result visualization. Thanks to the generic software architecture and the mutual inte- gration with the open-source project Java Proteomic Library (JPL), the addition of new methods to SmileMS is facilitated. Such methods in- clude quantification, the combined use of several algorithms, GC-MS and exact mass identification.