Based on attachment theory, the theory of thriving through relationships describes the interpersonal process of relational catalyst (RC) support (i.e., emotional or practical support that is responsive to the recipient’s needs) for life’s opportunities in the absence of adversity. While existing literature is clear that partner support positively predicts goal outcomes overall, relatively little is known about by how much, for whom, in which kinds of relationships, and under what conditions partner support is beneficial for goal outcomes. I investigate these research questions in this six-paper thesis. I begin by evaluating the existing literature in a meta-analysis (Manuscript 1). I establish that partner support moderately and positively predicts goal outcomes. Responsive and practical support were equally effective providing support for the theory of thriving through relationships. In Manuscript 2, I use state-of-the-art machine learning techniques to identify the most important individual (attachment avoidance, well-being) and relational (relationship satisfaction, trust, commitment, empathy) predictors of partner support. Manuscript 3 focuses on the link between goal conflict and the RC support process (seeking, perceiving, and providing support as well as pursuing life’s opportunities). In three studies, I show that high goal conflict is a strong negative predictor of all parts of the RC support process. The final three manuscripts extend the thriving through relationships framework by showing that RC support can still be beneficial even in the presence of adversity (COVID-19). Manuscript 4 shows that RC support is effective for goal outcomes during the pandemic. I also show that goal conflict is negatively associated and successful negotiation of goal conflict positively associated with partner support (Manuscript 5) and goal outcomes (Manuscript 6). Together these studies provide robust evidence for the importance of partner support for goal outcomes and highlight several individual, relational, and contextual factors that predict the effectiveness of the support.