One of the tasks where learned molecular features can be used is a Quantitative Structure-Activity Relationship (QSAR) problem. Based on the neighbourhood principle, QSAR can be used to assess the quality of the obtained molecular representation. In this way, it is expected that the predictions on latent vectors of order-dependent autoencoders will be lower than those of order-independent autoencoders. The data used for the QSAR benchmarking was collected from the ChEMBL v.27 database. More specifically, the activity information about targets for "Homo sapiens" organism with assay type "Binding assay" and target type "Single protein" was extracted. The obtained data consisted of 387K activity records for 1805 targets. Then, activity records were filtered if 1) they were measured for compounds that did not pass standardisation; 2) if the standard deviation of several IC50 concentrations for the same target and compound was more than 20 nM. It must be noticed that stereo marks were removed during standardisation, so the filtering process also excluded stereo active compounds' records. The remaining measurements for the "compound-target" pairs were averaged. IC50 thresholds were calculated for activity class labelling based on the number of active and inactive compounds for each target according to the protocol . The protocol consists of three steps. First, an active IC50 threshold is selected from the range [10 nM, 50 nM, 100 nM, 300 nM, 500 nM, 700 nM, 1 µM] to determine at least 15 active compounds. Then, an inactive IC50 threshold of 30% or 15 compounds is chosen. Finally, if the threshold for inactive compounds is more than ten times the threshold for active compounds, the threshold for active compounds is updated as the inactive threshold divided by 10 to collect more active molecules.