Developing new fluorophores needed for advanced bioimaging techniques requires the exploration of previously unexplored chemical space. Generative AI approaches for the creation of novel dye scaffolds are promising in that they explore diverse regions of chemical space, but previous attempts have yielded synthetically intractable dye candidates due to the absence of reaction constraints, thus impeding experimental validation. Here, we present SyntheFluor, a generative AI model that employs known reaction libraries and molecular building blocks to create readily synthesizable fluorescent molecule scaffolds. SyntheFluor designed 11,590 molecules, which were filtered to a set of 19 diverse candidate molecules predicted to have dye-like properties. These 19 candidates were further examined by time-dependent density functional theory calculations, and 14 were successfully synthesized and 13 were experimentally validated. The photophysical properties of the three most fluorescent molecules were characterized in depth, and the top scaffold in particular showed robust fluorescence properties comparable to a known dye, demonstrating the utility of SyntheFluor.