Ever-increasing design complexity and advances of technology impose great challenges on the design of modern microprocessors. One such challenge is to determine promising microprocessor configurations to meet specific design constraints, which is called Design Space Exploration (DSE). In the computer architecture community, supervised learning techniques have been applied to DSE to build regression models for predicting the qualities of design configurations. For supervised learning, however, considerable simulation costs are required for attaining the labeled design configurations. Given limited resources, it is difficult to achieve high accuracy. In this article, inspired by recent advances in semisupervised learning and active learning, we propose the COAL approach which can exploit unlabeled design configurations to significantly improve the models. Empirical study demonstrates that COAL significantly outperforms a state-of-the-art DSE technique by reducing mean squared error by 35% to 95%, and thus, promising architectures can be attained more efficiently.