Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient ones. Moreover, cross-linking complements several structure determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces two new concepts: (i) the cross-linked peptides are represented in the search database such that the cross-linking sites are explicitly encoded, and (ii) the scoring function derived from the Andromeda algorithm was adapted to score against a theoretical MS/MS spectrum that contains the peaks from all possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was evaluated against the recently published Kojak and the popular pLink algorithms on a calmodulin-plectin complex data set, as well as three additional, published data sets. The results show that Xilmass typically had the highest number of identified distinct cross-linked sites and also the highest number of predicted cross-linked sites.