Multimodal bioimaging combines multiple imaging techniques to provide a comprehensive view of biological structures and processes. This approach has the potential to revolutionize research in spatial biology by integrating visualization of complex processes across scales from single molecules to whole organisms. However, challenges remain in terms of technical complexity, data integration, and workflow standardization. The article discusses the current state of multimodal bioimaging, including direct and indirect approaches, and highlights key challenges such as hardware limitations, sample preparation, and data analysis. It also emphasizes the importance of developing open access infrastructure, fostering collaboration, and promoting training to overcome these barriers. The article proposes several solutions, including the development of standardized workflows, the use of machine learning for data analysis, and the creation of community-driven initiatives to advance the field. It also highlights the importance of integrating multimodal bioimaging with complementary spatial 'omics' approaches to gain deeper insights into biological systems. The article concludes with recommendations for future research and development, emphasizing the need for continued collaboration and innovation to fully realize the potential of multimodal bioimaging.Multimodal bioimaging combines multiple imaging techniques to provide a comprehensive view of biological structures and processes. This approach has the potential to revolutionize research in spatial biology by integrating visualization of complex processes across scales from single molecules to whole organisms. However, challenges remain in terms of technical complexity, data integration, and workflow standardization. The article discusses the current state of multimodal bioimaging, including direct and indirect approaches, and highlights key challenges such as hardware limitations, sample preparation, and data analysis. It also emphasizes the importance of developing open access infrastructure, fostering collaboration, and promoting training to overcome these barriers. The article proposes several solutions, including the development of standardized workflows, the use of machine learning for data analysis, and the creation of community-driven initiatives to advance the field. It also highlights the importance of integrating multimodal bioimaging with complementary spatial 'omics' approaches to gain deeper insights into biological systems. The article concludes with recommendations for future research and development, emphasizing the need for continued collaboration and innovation to fully realize the potential of multimodal bioimaging.