The increasing number of cancer cases and the prevalence of failed treatments underscore the need for alternative therapeutics and more predictive screening methods. In this context, the field of 3D cultures (termed spheroid, organoid, or tumoroid models) has seen rapid growth in recent years, leading to a range of more physiologically relevant models compared to standard 2D culture methods. However, many of these models face limitations in scalability due to their complex setup, maintenance requirements, and high costs, which restrict their use in drug discovery. In response, we present a simple but robust spheroid model for two distinct types of solid tumors, colorectal cancer and retinoblastoma, specifically designed for high-throughput drug screening. This model is reproducible and cost-effective, utilizing commercially available components and automation. We applied this model to screen chemotherapeutics and used high-content image-based analysis to identify prospective drug candidates. This spheroid model has the potential to advance drug discovery, particularly in challenging areas of cancer research.