Over the years, a number of search algorithms have been proposed in AI literature, ranging from best-first to depth-first searches, from incomplete to optimal searches, from linear memory to unbounded memory searches; each having their strengths and weaknesses. The variability in performance of these algorithms makes algorithm selection a hard problem, especially for performance critical domains. Algorithm portfolios alleviate this problem by simultaneously running multiple algorithms to solve a given problem instance, exploiting their diversity. In general, the portfolio methods do not share information among candidate algorithms. Our work is based on the observation that if the algorithms within a portfolio can share information, it may significantly enhance the performance, as one algorithm can now utilize partial results computed by other algorithms. To this end, we introduce a new search framework, called Search Portfolio with Sharing (SP-S), which uses multiple algorithms to explore a given state-space in an integrated manner, seamlessly combining the partial solutions, while preserving the constraints/characteristics of the candidate algorithms. In addition, SP-S can be easily adopted to guarantee theoretical properties like completeness, bounded sub-optimality, and bounded re-expansions. We describe the basics of the SP-S framework and explain how different classes of search algorithms can be integrated in SP-S. We discuss its theoretical properties and present experimental results for multiple domains, demonstrating the utility of such a shared approach.