Here we suggest a simple strategy to leverage the geometry of the VAE-learned latent space to produce novel variants of haloalkane dehalogenases (HLDs; EC 3.8.1.5). These enzymes cleave the carbon-halogen bonds and are widely used in biocatalysis, biosensing, cell imaging, and protein analysis . The proposed workflow is based on a small number of proteins with known functions and aims to produce new variants that preserve catalytic function and improve stability (Fig. ). First, we mined sequences with preserved catalytic residues using the EnzymeMiner to obtain an MSA of functionally related proteins. Second, we trained a VAE and specified several metrics to measure its capacity to generate protein sequences and capture the phylogeny in the constructed latent representations. Third, based on the geometry of the latent space, we developed a sampling strategy and produced a statistical profile of candidate sequences to select promising variants from the evolutionary trajectory. Fourth, we over-expressed and characterized variants experimentally using advanced microfluidics. Three consecutive rounds of experimental characterization and workflow optimization resulted in 20 variants, sharing as little as 67% sequence similarity to known HLDs. Obtained enzymes showed up to a 9°C increase in melting temperatures and an average improvement of 3°C across all soluble variants. We also observed a boost in activity, up to 3.5-fold for the most stable variant, whereas most of the other expressed variants showed activity levels comparable to benchmark enzymes.