Objectives: The aim of the first (major) part of this study was to identify and evaluate the most important in silico databases, that are commonly used by VarSome, regarding basic principles of function, classification output (scores and thresholds), web link and PMID number. The second (minor) aim of this study was to investigate in a pilot study, how well the classifications of yet unidentified genomic alterations in oncological samples, predicted by in silico databases, are congruent with the classifications determined via careful literature-based single variant analysis by the molecular oncology of the department of pathology at the medical center in Coburg. Materials and methods: In the first (major) part of this study, 92 VUS (variants of unknown significance), were entered into VarSome and the most important ones were identified. The results were clearly presented in the form of two tables. The second (minor) part of this work was conducted in form of a pilot study. 58 genetic variants were entered into VarSome for prediction. The predictive results (benign/pathogenic) were compared to the classifications determined by the department of pathology. Accuracy and MCC score were determined for each in silico database. A MCC score of 0.5 or higher was set as the threshold for acceptability, which is equivalent to an accuracy of >75%. Results: 20 in silico databases were identified as the most important prediction tools commonly used by VarSome (BayesDel addAF, BayesDel noAF, DEOGEN2, MetaLR, MetaRNN, MetaSVM, REVEL, EIGEN PC, FATHMM, FATHMM-MKL, FATHMM-XF, LIST-S2, LRT, Mutation Assessor, MutationTaster2, PrimateAl, PROVEAN, SIFT, SIFT 4G). The classification output (scores, thresholds), assigned to each tool, gives the possibility for a deeper understanding of predictive results. Further research can be quickly achieved by following the web link or PMID number determined for each in silico database. In the pilot study, the testing of congruency was indicative, but remained low in congruency. This was reflected in an MCC score, that remained overall below the acceptability value of 0.5. Conclusion: The results of the first (major) part of this work enable pathologists to extend the knowledge about interpreting and validating in silico databases, that are frequently used at the molecular oncology of the department of pathology at the medical center Coburg. The results of the second (minor) part, the pilot study, have contributed to get a feeling for the reliability of in silico databases. However, careful literature-based single variant analysis cannot be replaced by predictions of in silico databases, yet.