Very recently, semi-factual explanations have emerged in Explainable AI (XAI) as a new and potentially important explanation strategy. Semi-factuals employ “Even if...” reasoning, as opposed to the “If only...” reasoning of counterfactuals. Counterfactuals inform users about what feature-differences lead to changes in an outcome (e.g., “if only you asked for a lower loan, you would have been successful.”), whereas semi-factuals inform them about what feature-differences lead to the outcome remaining the same (e.g., “Even if you asked for a lower loan, you would still have been unsuccessful”). Semi-factuals have the potential to be as important as their popular counterfactual siblings. However, the AI/ML and XAI communities have by and large struggled to imagine useful application-scenarios for semi-factuals. In this paper, we summarize recent work on semi-factual explanation and trace a roadmap for application-focused research in the area. We begin by outlining the main constraints identified for semi-factual optimization proposed in the literature, before summarizing the applications of semi-factuals proposed to-date. Then, we sketch several directions for future applications and research using semi-factuals. Finally, though semi-factuals are highly promising (especially with regard to algorithmic recourse), they have a potential for ethical misuse that we discuss in our conclusions.