Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure. Biomarker candidates that are discovered by appropriate technologies such as unbiased mass spectrometry cannot be validated or translated to immunoassays in many cases. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important to allow routine clinical use. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, we present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development, while minimal time and effort are required.