We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs. We show how congregations can be used to explain and predict the behavior of self-interested agents that are searching for other agents to interact with. This framework is integrated with Vidal and Durfee's CLRI framework [11] for evaluating learning within MAS. We provide experimental and analytical results, which describe how the difficulty of the congregating problem increases exponentially with the number of agents, and present a solution to this in the form of labelers, which are agents that assign a description to a congregation, thereby reducing agents' search problem.