We have used three initialization approaches and two methods of structure optimization for the prediction of molecules from the test dataset. That resulted in six methods for searching molecules with prescribed property values. We had to choose a method of initialization of the embedding vector that is to be fed to the gradient descent procedure described above. A good initialization of an embedding vector is of crucial importance to the result of the algorithm since the embedding space can have multiple minima -say optimal molecules -or no minimum for a given output value. Also, there is no intrinsic dependability that the local extreme must correspond to the molecule with the best fitting property. Fortunately, due to the inherent properties of VAE architecture, similar structures tend to locate close to each other in the embedding space.