In this paper we study search strategies of agents that represent buyer agents’ coalitions in electronic marketplaces. The representative agents operate in environments where numerous potential complex opportunities can be found. Each opportunity is associated with several different terms and conditions thus differing from other opportunities by its value for the coalition. Given a search cost, the goal of the representative agent is to find the best set of opportunities which fulfills the coalition’s demands with the maximum overall utility, to be divided among the coalition members. Given the option of side-payments, this strategy will always be preferred by all coalition members (thus no conflict of interests), regardless of the coalition’s payoff division protocol. We analyze the incentive to form such coalitions and extract the optimal search strategy for their representative agents, with a distinction between operating in B2C and C2C markets. Based on our findings we suggest efficient algorithms to be used by the representative agents for calculating their strategy and the appropriate derived expected utilities. A computational-based example is given, illustrating the achieved performance as a function of the heterogeneity level of the coalition’s members.