Pinterest developed a dedicated candidate generation model for conversion ads to address challenges with offsite conversion data sparsity and noise. This model differs from previous engagement-based systems by focusing on lower-funnel conversions. The initial launch in 2023 yielded significant improvements in both conversion and engagement metrics, including a higher clickthrough rate. Further iterations in 2025 delivered even greater conversion value and enhanced advertiser return on ad spend. To combat data sparsity, the model is trained across all shopping surfaces using a multi-surface approach. It supplements primary conversion signals with onsite engagement data, re-weighting click data based on duration to mitigate noise. Harder negatives, such as ad impressions with no engagement, are used for more robust contrastive learning. The model incorporates user-side features capturing real-time intent and long-term preferences, alongside Pin-side features for semantic understanding and performance tracking. A two-tower architecture with DCN v2 and an MLP in parallel cross layers enhances feature interaction modeling and retrieval quality. The model evolved from a multi-head design to a unified multi-task architecture, allowing direct benefit from multi-task optimization during serving. An advertiser-level loss function was introduced to provide a more stable granularity for conversion signals, leading to substantial recall improvements. This new model successfully increased shopping conversion volume and improved advertiser performance while enhancing the user shopping experience.