Nowadays the most used pipeline for protein identification consists in the comparison of the MS/MS spectra to reference databases. Search algorithms compare obtained spectra to an in silico digestion of a sequence database to find exact matches. In this context, the database has a paramount importance and will determine in a great deal the number of identifications and its quality, being this especially relevant for non-model plant species. Using a single Viridiplantae database (NCBI, UniProt) and TAIR is not the best choice for non-model species since they are underrepresented in databases resulting in poor identification rates. We demonstrate how it is possible to improve the rate and quality of identifications in two orphan species, Quercus ilex and Pinus radiata, by using SEQUEST and a combination of public (Viridiplantae NCBI, UniProt) and a custom-built specific database which contained 593,294 and 455,096 peptide sequences (Quercus and Pinus, respectively). These databases were built after gathering and processing (trimming, contiging, 6-frame translation) publicly available RNA sequences, mostly ESTs and NGS reads. A total of 149 and 1533 proteins were identified from Quercus seeds and Pinus needles, representing a 3.1- or 1.5-fold increase in the number of protein identifications and scores compared to the use of a single database. Since this approach greatly improves the identification rate, and is not significantly more complicated or time consuming than other approaches, we recommend its routine use when working with non-model species.