It is well known that the cholinergic deficiency contributes to the cognitive deterioration of Alzheimer’s Disease (AD) patients and that its pathways in the cerebral cortex are also compromised. In this work we use computational tools to design new and more powerful inhibitors of acetylcholinesterase (AChe). We made use of the de novo design and fragment-based drug design (FBDD). In the former approach, we started from reference drugs used in the AD treatment. These drugs were break into small pieces (fragments). These fragment were used as seed to grown new molecules or to be linked with other new fragments. In the latter approach, a library of fragments is docked in the active site of the enzyme. The interaction of each fragment is measured and they are organized by their affinity. The best ranked fragment are them linked between them to form new molecules with high degree of interaction with the active site of the enzyme. Using this strategy, we were able to produce a library of 2M new molecules. This library was filtered using as criterion the adsorption, distribution, metabolism and excretion (ADME) properties. The resulting library with around 6k molecules is filtered again using the Tanimoto similarity coefficient (structures with values greater than 0.85 were eliminated). The final library with 1.5k compound was submitted to docking studies. Finally, 10 compounds with better interaction energies than the reference compounds were obtained.