The two-way Information Bottleneck problem, where two nodes exchange information iteratively about two arbitrarily dependent memoryless sources, is considered. Based on the observations and the information exchange, each node is required to extract “relevant information”, measured in terms of the normalized mutual information, from two arbitrarily dependent hidden sources. The optimal trade-off between rates of relevance and complexity, and the number of exchange rounds, is obtained through a single-letter characterization. We further extend the results to the Gaussian case. Applications of our setup arise in the development of collaborative clustering algorithms.