Augmented SBERT is a data augmentation method that addresses limitations in existing bi-encoders for pairwise sentence scoring tasks. It uses data augmentation to generate new sentence pairs and employs seed optimization, training multiple models with varied seeds to identify the best one. The approach achieves significant performance improvements, with gains of up to 6 points in in-domain tasks and 37 points in domain adaptation scenarios. AugSBERT outperforms traditional methods like cross-encoders and bi-encoders by creating high-quality sentence embeddings through data augmentation. It is efficient and scalable, making it a practical solution for accurate and scalable sentence-scoring tasks. The approach uses sampling strategies to select relevant pairs, including BM25, Kernel Density Estimation (KDE), Random Sampling, Semantic Search, and their combinations. AugSBERT's performance is comparable to cross-encoders in some cases but outperforms them in others. It is particularly effective when adapting from generic to specific domains. The method relies on computationally intensive sampling strategies, such as KDE, which may limit scalability in large-scale implementations. Future research directions include exploring more efficient sampling methods and improving its performance in multilingual and low-resource settings.