The abundance of scientific articles published and indexed in publicly accessible repositories has spurred the research and development of automated information extraction systems. The output of such systems can be used to assemble large networks capturing the understanding of mechanistic pathways and their interactions as represented in the underlying body of research.We describe a system designed to help researchers search, visualize and interact with biological networks derived via information extraction tools. As input, the system takes a dataset of biological and biochemical interactions automatically generated by an information extraction system and provides an interface designed to search, visualize and interact with the data. The usage paradigm consists of identifying a starting point for a search, then using the data's network structure by incrementally exploring the immediate neighborhood of the elements displayed by the system.Our system differs from prior work as it leverages both the network structure in the data and the natural language text backing those connections: every connection displayed is traceable back to the documents and phrases in the corpus that support that specific piece of information. We also present two case studies with immunobiology researchers using the system to find previously unknown relationships between biological entities. While the evidence suggesting these relationships already existed, it was scattered across the literature, and existing specialized web databases and domain-search engines could not find it. The system is open-source, with the code publicly available on GitHub.