Existing analytical tools enable broad-scale experimentation ("-omics") to provide a great deal of information about intracellular processes. However, extraction of information regarding intercellular interactions, particularly from separate datasets, is generally more limited, principally for lack of specialized analytical tools. In turn, few experiments are designed to examine intercellular interactions. Using the large number of previously identified interactions available in databases may provide a useful platform for analyzing these interactions. However, finding all possible interactions is a computationally intensive task and quickly becomes intractable using a naive approach on networks with hundreds of thousands of nodes and edges. A heuristic algorithm similar to the "Backtracking algorithm" is proposed to find all possible protein interactions across any two gene sets. The algorithm starts with an initial set of genes and incrementally adds a candidate to the interaction network and abandons each candidate x as soon as it is determined that x does not lead to a valid solution. An exclusion vector (EV) is used to accomplish this task and is populated at each step, maintaining a list of those nodes that need to be excluded from interactions in the future and thus restricting the size of the network. The EV also allows location awareness by using Gene Ontology (GO) cell component classifications to discard nodes that are not relevant for the network. This algorithm can be readily applied to pathway analysis and the determination of elements underlying intercellular interactions.