Objectives of innovative design of complex systems involve searching for objects, characterized by the maximum degree of originality, and, consequently, competitiveness. To solve this problem, it is necessary to apply the methodology of cluster analysis that uses mathematical measures of similarity and inclusion of sets. Information about the analyzed object class is represented in the form of a morphological matrix, columns of which are the options for the implementation of generalized subsystems. For variants of subsystems, represented in the rows, is determined by numbers of structural and functional features. Selection of promising solutions uses criteria: matching originality determines the novelty for this class of combinations of functional subsystems; the originality of the elemental determines the number of new for this class of implementations of the functional subsystems involved in this combination; the measure of inclusion of features in the many other signs of the synthesized structures characterizes the degree of its originality. Generalized criterion of the promising solutions is the target function, the structure of which includes the criteria and the coefficients of their significance. The search technique decisions based on the calculation of a measure of inclusion consists a series of stages. During implementation the formal model, morphological matrix is constructed; for each trait the i-th row in the column corresponding to alternative implementations of the subsystem, is binary rating enable option. The relative number of matching relations is calculated; a matrix of the elemental composition of solutions is built; a description of the decisions by functional features is built; measures which enable the features of each object in the array of attributes of all synthesized solutions are determined. The analysis results are shown in the component table; they identify the objective function value, i.e. the degree of perspective of all possible solutions in the morphological matrix. The solution with the maximum objective function value is selected as the most promising for further analysis.