The EU ProteomeBinders coordination action helped define community standards for reporting and comparing binder and binder-target properties, with an eye toward quality control. This included reporting standards, an ontology for unambiguously describing binders and their properties, and a database schema for storing the experimental molecular interaction evidence used to support claims about those properties. But it left unresolved the question of how best to enable binder producers to submit those data, and to extract meaningful intelligence from them. We report on two studies we undertook to address these issues. The first problem we investigate is how to make the submission of experimental evidence in support of a binder easier for data producers. All data providers have data in different formats, depending on their experimental protocols and what kind of LIMS the use (if any). This is essentially a data integration problem, that ideally one could solve by leveraging existing tools. Indeed, the HUPO Proteomics Standards Initiative (PSI) defines community standards for data representation in proteomics to facilitate data comparison, exchange and verification. The MITAB25 format is part of the PSI-MI 2.5 standard and describes information about molecular interactions. To test whether this generic approach could be adapted to the specific needs of affinity proteomics, we converted a large sample of several hundred binders of different kinds (both antibodies and aptamers), and with different degrees of experimental support. This allowed us to import the data using PSICQUIC tool, developed by the MI workgroup, straightforwardly providing standard web services and search using MIQL language. We identified a number of challenges in forcing binder-target data to conform to MITAB25. The second problem is extracting intelligence from these data. Using the approach developed by Julie Bourbeillon, we extracted RDF triples from the database and injected these relations into two difference data stores. Using this system we are able to uniformly access binders from different technologies and from different data providers, compare binders properties, and define compatible binder sets. We report on implementing these strategies using high-level queries in the SPARQL language and rule-based reasoning using SWRL.