High-throughput screening using automated microscopes is a key driver in biopharma drug discovery, enabling the parallel evaluation of thousands of drug candidates for diseases such as cancer. Traditional image analysis and deep learning approaches have been employed to analyze these complex, large-scale datasets, with cell segmentation serving as a critical step for extracting relevant structures. However, both strategies typically require extensive manual parameter tuning or domain-specific model fine-tuning. We present a novel method that applies a segmentation foundation model in a zero-shot setting (i.e., without fine-tuning), guided by an in-context learning strategy. Our approach employs a three-step process for nuclei, cell, and subcellular segmentation, introducing a self-prompting mechanism that encodes morphological and topological priors using growing masks and strategically placed foreground/background points. We validate our method on both standard cell segmentation benchmarks and industry-relevant hit validation assays, demonstrating that it accurately segments biologically relevant structures without the need for dataset-specific tuning.