Pinterest's ads aim to inspire users and seamlessly integrate into their shopping journeys. Understanding rapidly evolving user behavior is key to surfacing relevant ads. Traditional targeting methods often miss the nuances of user intent. The Pinterest Ads team developed advanced behavioral sequence modeling for improved ad candidate generation. Initially, a transformer-based model predicted advertisers users would interact with next. This advertiser-level model achieved significant lifts in conversion volume and reduced CPA in production. Building on this, the team developed an item-level model to predict specific products users would engage with. This model uses rich Pin embeddings and catalog metadata for granular representations. The item-level model also demonstrated substantial improvements in user checkout performance and reduced CPA. Learnings involved addressing popularity bias, handling sparse features, and optimizing sequence length.