By searching the given image in Yandex gives us the similar photos could be found in the "molecularshirts.com"To find the picture we provided i decided to download all the photos in the site.I automated the process by using this script.```import osfile = open("molecules","r")for line in file:    uri= "http://www.molecularshirts.com/wp-content/themes/poza-child/images/names/"+line[:-1]+".jpg"    os.system("wget "+uri)```When the script running on another terminal i used this script to compare photos with the provided image.```import cv2import numpy as np
original = cv2.imread("1.png")file = open("molecule_names","r")for line in file:    image_to_compare = cv2.imread(line[:-1]+".jpg")
    if original.shape == image_to_compare.shape:        print("The images have same size and channels")        difference = cv2.subtract(original, image_to_compare)        b, g, r = cv2.split(difference)
        if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:            print("The images are completely Equal")        else:            print("The images are NOT equal")
    sift = cv2.xfeatures2d.SIFT_create()    kp_1, desc_1 = sift.detectAndCompute(original, None)    kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None)
    index_params = dict(algorithm=0, trees=5)    search_params = dict()    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(desc_1, desc_2, k=2)
    good_points = []    for m, n in matches:        if m.distance < 0.6*n.distance:            good_points.append(m)
    number_keypoints = 0    if len(kp_1) <= len(kp_2):        number_keypoints = len(kp_1)    else:        number_keypoints = len(kp_2)     if(len(good_points)>300):        print("File Name:"+line)        print("GOOD Matches:", len(good_points))        print("How good it's the match: ", len(good_points) / number_keypoints * 100)
    result = cv2.drawMatches(original, kp_1, image_to_compare, kp_2, good_points, None)
    cv2.destroyAllWindows()```Then Voila!