Analytical sociology is beginning to embrace a digital revolution in the collection and analysis of social data and is increasingly drawing on tools from computational social science (CSS) to pursue its goals of mechanism-based explanation of aggregate outcomes. In this chapter, we highlight the ways in which analytical sociologists are using CSS tools to further social research. Using agent-based modeling, large-scale online experiments, digital trace data, and natural language processing, analytical sociologists are identifying how large-scale properties of social systems emerge from the complex interactions of networked actors at lower scales. At the same time, we provide a perspective on how CSS techniques can be successfully deployed in social research, including ways in which they can be productively combined. Computational tools, when applied using a theory-grounded approach, offer sociologists a chance to transcend the limitations of the dominant survey-research paradigm and finally address “big” sociological questions about, for example, the nature of culture, the emergence of inequality, and the dynamics of segregation. We also discuss how computational social scientists can take cues from analytical sociology to further hone their own research and methods in the service of theoretically grounded, mechanism-based explanations, moving beyond theoretically thin descriptions or predictions of micro- and macro-level outcomes.