Developing new materials for specific applications is an active field of research for both material science and quantum chemistry communities. The number of atomic compositions of molecular structures scales combinatorically with the size of the molecules, limiting the efficiency of classical algorithms. On the other hand, quantum computers can provide an efficient solution to the sampling of the chemical compound space. In this talk we propose a quantum algorithm with favorable scaling in resource requirements, allowing for the solution of the material design problem in currently available noisy quantum processors. The proposed scheme divides the problem into a classical optimization and a quantum search problem within a hybrid quantum classical algorithm. We demonstrate both in simulations (with and without noise) and in IBMQ quantum hardware the efficiency of our scheme and highlight the results in a few test cases. These preliminary results can serve as a basis for the development of further material design quantum algorithms for near term quantum computers.