Risk-based investing is experiencing growing success among investors, although some critics contend that the implicit “no-views” characteristic of these solutions might trigger other forms of risk, such as valuation risk. In this article, the authors introduce an analytical framework that allows investors to add active views on top of a risk-based solution, bridging the gap between risk-based investing and mean-variance portfolio optimization. Starting from a Black-Litterman approach, the authors derive closed-form expressions for the active risk-based portfolio weights and discuss practical implementation aspects. The framework is illustrated with a multi-asset allocation exercise over the period 1974–2016. Using views generated from macroeconomic regime signals, the active risk-based strategy is shown to outperform empirically both passive risk-based strategies and popular methodologies such as Equal-weight or Maximum Sharpe ratio.