A different initialization of a model training and stricter threshold of column removal with query amino acid positions in the third round led to a second VAE model, which generated sequences with an implicit His-Tag and high similarity to proteins with known experimental structures. Investigating the sequence reconstructed from the origin of the latent space (AncDhaA20) unveiled a notable shift in similarity towards another wild-type dehalogenase, DbjA , altered substrate specificity, increased thermostability (60°C) and improved activity (3.5-fold), being a top-performing haloalkane dehalogenase (Fig. ). To better understand the sensitivity of the straight-line evolutionary strategy to different initializations, we examined the embeddings over an ensemble of four randomly initialized VAEs (Section 3 Model ensemble trajectories of SI, Fig. ). Although we observed a general trend that the straight-line evolutionary trajectories converged towards the origin of the latent spaces of different VAEs, it was also evident that the trajectories exhibited quite wide scatter. This suggests that ensemble learning might be an interesting direction for follow-up research to improve the robustness of our strategy.