Our study demonstrated that the structure of the latent space and the generative potential of VAEs are capable of guiding the sequence search and designing novel soluble and functional proteins with enhanced stability. The workflow underwent systematic improvements through three consecutive design-build-test phases, with each iteration informed by the findings from the previous one. Detailed experimental characterization of designed variants by in-house microfluidics was instrumental for iterative improvement of our computational workflow and optimization of selection strategy. The success rate of soluble designs increased from 11% in the first round to 66% in the second round and 75% in the third round. Such an improvement of the protocol would not be achievable without gaining and implementing new knowledge based on the data collected using the wet lab experiments. Through this process, complemented by manual curation of specific variants, we achieve a notable increase in stability-up to 9°C for the top-performing AncDhaA20 variant, with an average improvement of 3°C across all soluble variants and a significant boost in activity up to 3.5-fold. The frequency and location of indels were the most critical parameters to consider during the design selection for experimental characterization. Using our approach, we recommend selecting designs with a low number of indels or with high protein similarity to natural sequences, preferably to those in PDB. A current limitation of our study is that it was conducted using a single enzyme family. Therefore, validation of designs for other protein families will help understand the generalizability of the developed approach. Overall, our study demonstrates that VAE represents a promising strategy for generating novel soluble, stable, and functional enzymes.